From 4c7acb66bb5f9e2995f25bfb65488c9d2ac98e94 Mon Sep 17 00:00:00 2001 From: Abdulrahman Tabaza <84684514+a-tabaza@users.noreply.github.com> Date: Fri, 24 May 2024 23:39:14 +0300 Subject: [PATCH] madi you are equally in the wrong here pick better pastimes. --- .gitignore | 6 - Assignments/Assignment1/Hmw1.ipynb | 267 ------- Assignments/Assignment1/solution.json | 59 -- Assignments/Assignment2/main.ipynb | 561 -------------- Assignments/Side Project/T_Proj.ipynb | 595 --------------- Binary Variables/BankLocation_Problem.ipynb | 68 -- Binary Variables/PartsWorth_Problem.ipynb | 39 - Binary Variables/notes.txt | 29 - Decision Analysis/CarLeases_Problem.ipynb | 218 ------ Decision Analysis/CarpoolRoute_Problem.ipynb | 0 .../ChardonnayOrRiesling_Problem.ipynb | 121 --- .../ClevelandToMyrtle_Problem.ipynb | 192 ----- Decision Analysis/DataWarehouse_Problem.ipynb | 96 --- .../DecisionTree_Winery.png | Bin 122305 -> 0 bytes .../DecisionTree_Winery_Sol1.png | Bin 50130 -> 0 bytes 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The time required\n", - "to complete job $j$ by employee $e$ using machine $m$ is given and denoted by $t_{jem}$. An employee can\n", - "only perform one job at a time. Similarly, a machine can only execute one job at a time. A job\n", - "cannot be divided among employees. Also, a job cannot be divided betwee machines. Develop a\n", - "mathematical formulation that can be used to obtain the optimal job-employee-machine\n", - "assignment if the company wants to minimize the total time spent by all machines to complete the\n", - "seven jobs. Generate different scenarios and, using Cplex, implement and solve your formulation\n", - "for these scenarios." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets and Indicies

\n", - "\n", - "$\\mathcal{J}$: A set representing the Jobs. $|\\mathcal{J}| = \\textbf{J}$
\n", - "$\\mathcal{E}$: A set representing the Employees. $|\\mathcal{E}| = \\textbf{E}$
\n", - "$\\mathcal{M}$: A set representing the Machines. $|\\mathcal{M}| = \\textbf{M}$
\n", - "\n", - "

Data

\n", - "\n", - "$T$: 3D-matrix of the time required to complete job $j$ by employee $e$ using machine $m$, where each element is denoted as $t_{jem}$.
\n", - "\n", - "

Decision Variable

\n", - "\n", - "$X$: 3D-binary-matrix of the which each employee in $\\mathcal{E}$ should spend using each machine in $\\mathcal{M}$ to complete every job in $\\mathcal{J}$, where each element is denoted as $x_{jem}$
\n", - "\n", - "\n", - "

Function

\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{minimize }} & \\; \\sum_{i=1}^{\\textbf{J}}\\sum_{u=1}^{\\textbf{E}}\\sum_{o=1}^{\\textbf{M}}{x_{iou}t_{iou}}\\\\\n", - "\\ \\text{subject to:} \\\\\n", - "& \\sum_{u=1}^{\\textbf{E}}\\sum_{o=1}^{\\textbf{M}}{x_{juo}} = 1 \\; ; \\forall j \\in \\mathcal{J}, \\\\\n", - "& x_{jem} \\in {0, 1}\n", - "\\end{align*}\n" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![image.png](attachment:image.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "import numpy as np\n", - "import json\n", - "\n", - "### Numbers too large to solve.\n", - "JOBS = 3\n", - "EMPLOYEES = 10\n", - "MACHINES = 5\n", - "\n", - "def get_model(T):\n", - " m = Model(name = 'Assignment')\n", - "\n", - " dv = m.binary_var_list(EMPLOYEES*MACHINES*JOBS, name = 'x')\n", - "\n", - " for i in range(JOBS):\n", - " summation = 0\n", - " for u in range(EMPLOYEES):\n", - " for o in range(MACHINES):\n", - " summation += dv[i*EMPLOYEES*MACHINES + u*MACHINES + o]\n", - "\n", - " m.add_constraint(summation == 1)\n", - "\n", - " function_summation = 0\n", - " T_flat = T.flatten()\n", - " for i in range(JOBS):\n", - " for u in range(EMPLOYEES):\n", - " for o in range(MACHINES):\n", - " function_summation += dv[i*EMPLOYEES*MACHINES + u*MACHINES + o] * T_flat[i*EMPLOYEES*MACHINES + u*MACHINES + o]\n", - "\n", - " m.minimize(function_summation)\n", - "\n", - " m.export_as_lp(\"Assignment_1.lp\")\n", - "\n", - " return m" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 33.000000 after 0.00 sec. (0.01 ticks)\n", - "Found incumbent of value 12.000000 after 0.01 sec. (0.01 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 3 rows and 150 columns.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.01 sec. (0.05 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.01 sec. (0.07 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.01 sec. (0.07 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=12,values={x_39:1,x_55:1,x_130:1})" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "np.random.seed(0)\n", - "\n", - "T = np.random.randint(4, 20, (JOBS, EMPLOYEES, MACHINES))\n", - "\n", - "model = get_model(T)\n", - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JobSolveStatus.OPTIMAL_SOLUTION\n", - "\n" - ] - } - ], - "source": [ - "print(model.solve_status)\n", - "print()\n", - "# model.solution.get_values(model.use_vars)\n", - "solution = json.loads(model.solution.export_as_json_string())\n", - "indicies = [int(index['index']) for index in solution['CPLEXSolution']['variables']]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[16 19 9 4 7]\n", - " [15 7 11 13 7]\n", - " [ 9 6 8 11 10]\n", - " [12 12 16 14 5]\n", - " [10 11 11 18 12]\n", - " [ 5 9 13 17 12]\n", - " [13 8 7 4 7]\n", - " [ 9 18 19 19 4]\n", - " [ 6 7 12 5 7]\n", - " [17 7 7 18 11]]\n", - "\n", - " [[ 4 5 13 13 19]\n", - " [ 4 19 14 8 11]\n", - " [ 7 18 15 6 11]\n", - " [16 6 4 4 8]\n", - " [ 9 9 10 12 8]\n", - " [ 5 19 8 13 14]\n", - " [14 19 12 5 5]\n", - " [11 13 13 7 10]\n", - " [11 15 18 6 15]\n", - " [ 4 18 7 9 16]]\n", - "\n", - " [[13 14 8 15 8]\n", - " [10 8 19 19 8]\n", - " [ 7 16 8 8 12]\n", - " [18 19 8 7 14]\n", - " [11 19 17 9 9]\n", - " [ 4 5 9 13 7]\n", - " [ 4 19 9 18 4]\n", - " [ 5 6 8 6 4]\n", - " [17 7 6 14 17]\n", - " [ 4 11 9 13 19]]]\n", - "Job 1 is assigned to Employee 8 on Machine 5 with a cost of 4\n", - "Job 2 is assigned to Employee 2 on Machine 1 with a cost of 4\n", - "Job 3 is assigned to Employee 7 on Machine 1 with a cost of 4\n" - ] - } - ], - "source": [ - "print(T)\n", - "\n", - "for i in range(JOBS):\n", - " for u in range(EMPLOYEES):\n", - " for o in range(MACHINES):\n", - " # print(indicies)\n", - " if (i*EMPLOYEES*MACHINES + u*MACHINES + o) in indicies:\n", - " print(f\"Job {i+1} is assigned to Employee {u+1} on Machine {o+1} with a cost of {T[i][u][o]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Question, can the same machines be operated in 2 different jobs\n", - "#### Question, can the same person complete 2 different jobs" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Assignments/Assignment1/solution.json b/Assignments/Assignment1/solution.json deleted file mode 100644 index f436e6d..0000000 --- a/Assignments/Assignment1/solution.json +++ /dev/null @@ -1,59 +0,0 @@ -{ - "CPLEXSolution": { - "version": "1.0", - "header": { - "problemName": "Assignment", - "objectiveValue": "1.0", - "solved_by": "cplex_local" - }, - "variables": [ - { - "index": "1", - "name": "x_1", - "value": "1.0" - }, - { - "index": "19", - "name": "x_19", - "value": "1.0" - }, - { - "index": "29", - "name": "x_29", - "value": "1.0" - }, - { - "index": "42", - "name": "x_42", - "value": "1.0" - }, - { - "index": "50", - "name": "x_50", - "value": "1.0" - } - ], - "linearConstraints": [ - { - "name": null, - "index": 0 - }, - { - "name": null, - "index": 1 - }, - { - "name": null, - "index": 2 - }, - { - "name": null, - "index": 3 - }, - { - "name": null, - "index": 4 - } - ] - } -} \ No newline at end of file diff --git a/Assignments/Assignment2/main.ipynb b/Assignments/Assignment2/main.ipynb deleted file mode 100644 index aabe7c4..0000000 --- a/Assignments/Assignment2/main.ipynb +++ /dev/null @@ -1,561 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CHeck \"LaRosa Machine Shop\" problem, location-like problem.\n", - "\n", - "Minimize sqrt((x-1)^2 + (y-2)^2) + sqrt((x-n)^2 + (y-4)^2) + ... \n", - "\n", - "Optimal answer = (2.230, 3.349)\n", - "\n", - "Part A: Solve LaRosa with derivation. (answer should be (2.230, 3.349))\n", - "\n", - "Part B: Solve LaRosa as binary Linear Problem: Do it by splitting the 6x6 into 36 grids, and calculating the distances for each one of the points prior to giving it to the model.\n", - " \n", - "for example, (0.5, 0.5) = 12, (1.5, 0.5) = 10, (2.5, 0.5) = 7, etc.. , and then its a binary problem. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us consider the case of LaRosa Machine Shop (LMS). LMS is studying where to locate\n", - "its tool bin facility on the shop floor. The locations of the five production stations appear in\n", - "Figure 14.9. In an attempt to be fair to the workers in each of the production stations, man-\n", - "agement has decided to try to find the position of the tool bin that would minimize the sum\n", - "of the distances from the tool bin to the five production stations. We define the following\n", - "decision variables:\n", - "\n", - "X = horizontal location of the tool bin\n", - "\n", - "Y = vertical location of the tool bin" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Important Note

\n", - "

The fast and optimzied solution I showed you is in the last cells. It was in Numpy but I changed it based on your request to DoCplex. Its still fast and accurate

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve with derivation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.3\n", - "3.4\n" - ] - } - ], - "source": [ - "from sympy import Symbol\n", - "from sympy.solvers import solve\n", - "import numpy as np\n", - "\n", - "Stations = [\"Fabrication\",\"Paint\",\"Subassembly 1\",\"Subassembly 2\",\"Assembly\"]\n", - "x_coords = [1,1,2.5,3,4]\n", - "y_coords = [4,2,2 ,5,4]\n", - "\n", - "# Solve => Minimize the sum of distances\n", - "# Euclidian Distance = sqrt(a^2-b^2)\n", - "# TRIED USING EUCLIDIAN DISTANCE, BUT THE DERIVATES BECOMES TOO COMPLEX TO SOLVE.\n", - "# HENCE, I SWITCHED TO MANHATTEN DISTANCE. \n", - "# Manhatten Distance = |a-b|\n", - "# BUT EVEN THIS DIDN'T WORK BECAUSE SYMPY DOESN'T KNOW HOW TO DERIVE abs\n", - "# HENCE, I FINALLY CREATED MY NEW VERY OWN EQUATION CALLED:\n", - "# Clidian distance = a^2-b^2\n", - "# WHICH IS JUST EUCLIDIAN DISTANCE BUT WITHOUT THE SQRT.\n", - "\n", - "distance = lambda x, y: ((x[0] - y[0])**2 + (x[1] - y[1])**2)\n", - "\n", - "X = Symbol('X')\n", - "Y = Symbol('Y')\n", - "\n", - "value = 0\n", - "for x_coord, y_coord in zip(x_coords, y_coords):\n", - " value += distance((X, Y), (x_coord, y_coord))\n", - "\n", - "\n", - "diffed_X = value.diff(X)\n", - "diffed_Y = value.diff(Y)\n", - "\n", - "print(float(solve(diffed_X, X)[0]))\n", - "print(float(solve(diffed_Y, Y)[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.scatter(x_coords, y_coords)\n", - "plt.scatter(float(solve(diffed_X, X)[0]), float(solve(diffed_Y, Y)[0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve as Integer Optimization " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 30/30 [00:00<00:00, 3323.27it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from tqdm import tqdm \n", - "\n", - "Stations = [\"Fabrication\",\"Paint\",\"Subassembly 1\",\"Subassembly 2\",\"Assembly\"]\n", - "x_coords = [1,1,2.5,3,4]\n", - "y_coords = [4,2,2 ,5,4]\n", - "\n", - "# WHATEVER YOU DO, DO NOT MAKE THIS LARGER THAN 1000. IT BROKE MY PC. PLEASE DONT\n", - "# ALSO, IF VALUE IS LARGER THAN 200. COMMENT THE LAST LINE IN THIS CELL.\n", - "# ALSO ALSO, IF THE VALUE IS LARGER THAN 5O. DO NOT SOLVE IT USING DOCPLEX.\n", - "expansion = 10 # Higher values mean more accurate output, but more computationally expensive\n", - "# TRIED RASING THE EXPANSION TO 500, AND GOT THE MOST CORRECT ANSWER WHICH IS:\n", - "# (2.229, 3.349) with a distance of 8.3006 units\n", - "\n", - "count_x_possible = int(max(x_coords)) - int(min(x_coords))\n", - "count_y_possible = int(max(y_coords)) - int(min(y_coords))\n", - "\n", - "plt.scatter(x_coords, y_coords)\n", - "\n", - "possibles_places_X= []\n", - "possibles_places_Y= []\n", - "possibles_places = []\n", - "\n", - "for x_indx in tqdm(range(int(count_x_possible*expansion))):\n", - " for y_indx in range(int(count_y_possible*expansion)):\n", - "\n", - " x_possible_coord = min(x_coords) + (x_indx + 0.5) / expansion\n", - " y_possible_coord = min(y_coords) + (y_indx + 0.5) / expansion\n", - "\n", - " possibles_places_X.append(x_possible_coord)\n", - " possibles_places_Y.append(y_possible_coord)\n", - " possibles_places.append((x_possible_coord, y_possible_coord))\n", - "\n", - "plt.scatter(possibles_places_X, possibles_places_Y, alpha=0.4)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 900/900 [00:00<00:00, 149293.00it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "900\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "euclidian_distance = lambda x, y: ((x[0] - y[0])**2 + (x[1] - y[1])**2)**0.5\n", - "distances = []\n", - "\n", - "for x_possible_coord, y_possible_coord in tqdm(possibles_places):\n", - " point_distance = []\n", - " for x_coord, y_coord in zip(x_coords, y_coords):\n", - " point_distance.append(euclidian_distance((x_possible_coord, y_possible_coord),\n", - " (x_coord, y_coord)))\n", - "\n", - " distances.append(point_distance)\n", - "\n", - "distances = np.array(distances)\n", - "print(len(possibles_places))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solve with NumPy (Ignore)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Distance: 8.300962617731178\n", - "Coords:\t (2.25, 3.35)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print('Distance:', distances[distances.sum(1).argmin()].sum())\n", - "print('Coords:\\t', possibles_places[distances.sum(1).argmin()])\n", - "\n", - "plt.scatter(x_coords, y_coords)\n", - "x_temp_solution, y_temp_solution = possibles_places[distances.sum(1).argmin()]\n", - "plt.scatter(x_temp_solution, y_temp_solution)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solve with DoCplex" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "from tqdm import tqdm\n", - "\n", - "m = Model()\n", - "\n", - "X_stations = m.binary_var_list(possibles_places)\n", - "\n", - "m.add_constraint(sum(X_stations) >= 1)\n", - "\n", - "m.minimize(sum([X_stations[i]*sum(distances[i]) for i in range(len(X_stations))]))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 8633.214624 after 0.02 sec. (0.04 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 1 rows and 900 columns.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.00 sec. (0.34 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.02 sec. (0.41 ticks)\n", - "Parallel b&c, 6 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.02 sec. (0.41 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model1\n", - "objective: 8.30096\n", - "status: OPTIMAL_SOLUTION(2)\n", - "(2.25, 3.35)=1\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(solution)\n", - "plt.scatter(x_coords, y_coords)\n", - "plt.scatter(2.25, 3.35)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Most accurate and Fastest solution (Personal)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 500/500 [00:07<00:00, 65.09it/s]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from tqdm import tqdm \n", - "import matplotlib.pyplot as plt\n", - "import json\n", - "\n", - "def solve_accurate(x_coords, y_coords, expansion, epochs, draw_plot =False, alpha = 0.2):\n", - "\n", - " min_pos_x, max_pos_x = min(x_coords), max(x_coords)\n", - " min_pos_y, max_pos_y = min(y_coords), max(y_coords)\n", - "\n", - " if draw_plot:\n", - " plt.scatter(x_coords, y_coords)\n", - "\n", - " distances_loss = []\n", - "\n", - " for epoch_num in tqdm(range(epochs)):\n", - "\n", - " possibles_places_X= []\n", - " possibles_places_Y= []\n", - " possibles_places = []\n", - "\n", - " for x_indx in np.arange(min_pos_x, max_pos_x, 1/(expansion*(epoch_num+1))):\n", - " for y_indx in np.arange(min_pos_y, max_pos_y, 1/(expansion*(epoch_num+1))):\n", - "\n", - " x_possible_coord = (x_indx)\n", - " y_possible_coord = (y_indx)\n", - "\n", - " possibles_places_X.append(x_possible_coord)\n", - " possibles_places_Y.append(y_possible_coord)\n", - " possibles_places.append((x_possible_coord, y_possible_coord))\n", - "\n", - " if draw_plot:\n", - " plt.scatter(possibles_places_X, possibles_places_Y, alpha = alpha)\n", - "\n", - " euclidian_distance = lambda x, y: ((x[0] - y[0])**2 + (x[1] - y[1])**2)**0.5\n", - " distances = []\n", - "\n", - " for x_possible_coord, y_possible_coord in possibles_places:\n", - " point_distance = []\n", - " for x_coord, y_coord in zip(x_coords, y_coords):\n", - " point_distance.append(euclidian_distance((x_possible_coord, y_possible_coord),\n", - " (x_coord, y_coord)))\n", - "\n", - " distances.append(point_distance)\n", - "\n", - " distances = np.array(distances)\n", - "\n", - " m = Model()\n", - "\n", - " X_stations = m.binary_var_list(possibles_places)\n", - "\n", - " m.add_constraint(sum(X_stations) >= 1)\n", - "\n", - " m.minimize(sum([X_stations[i]*sum(distances[i]) for i in range(len(X_stations))]))\n", - " \n", - " solution = m.solve(log_output = False)\n", - "\n", - " best_index = (json.loads(solution.export_as_json_string())['CPLEXSolution']['variables'][0][\"index\"])\n", - "\n", - " x_temp_solution, y_temp_solution = possibles_places[int(best_index)]\n", - "\n", - " min_pos_x = x_temp_solution-(1/(epoch_num+1))\n", - " min_pos_y = y_temp_solution-(1/(epoch_num+1))\n", - "\n", - " max_pos_x = x_temp_solution+(1/(epoch_num+1))\n", - " max_pos_y = y_temp_solution+(1/(epoch_num+1))\n", - "\n", - " return possibles_places[distances.sum(1).argmin()], distances_loss, 1/(expansion*(epoch_num+1))\n", - "\n", - "\n", - "Stations = [\"Fabrication\",\"Paint\",\"Subassembly 1\",\"Subassembly 2\",\"Assembly\"]\n", - "x_coords = [1,1,2.5,3,4]\n", - "y_coords = [4,2,2 ,5,4]\n", - "\n", - "expansion = 12\n", - "\n", - "solution_coords, loss, last_step_size = solve_accurate(x_coords, y_coords, 5, 500, True, 0.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.229650636905417, 3.348835650450712)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution_coords" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Assignments/Side Project/T_Proj.ipynb b/Assignments/Side Project/T_Proj.ipynb deleted file mode 100644 index c465f86..0000000 --- a/Assignments/Side Project/T_Proj.ipynb +++ /dev/null @@ -1,595 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "write the cplex code for this,\n", - "\n", - "fill an n x n matrix W with random numbers,\n", - "\n", - "define two variable matrices, A and B, with one being n x r • r x n,\n", - "\n", - "optimize for the best r and entries in A and B such that their product contains an approximation of W using some arbitrary loss" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from docplex.cp.model import CpoModel as Model\n", - "from docplex.cp.parameters import CpoParameters\n", - "\n", - "n = 32\n", - "r = 8\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(0)\n", - "\n", - "# myparams = CpoParameters()\n", - "m = Model()\n", - "\n", - "W = np.random.rand(n, n).round(6)\n", - "\n", - "A = np.array(m.integer_var_list(r*n, 0, 1000))\n", - "B = np.array(m.integer_var_list(n*r, 0, 1000))\n", - "\n", - "for i in range(n*r):\n", - " m.add_constraint(A[i] * B[i] <= 1_000_000)\n", - " m.add_constraint(A[i] * B[i] >= 0)\n", - "\n", - "\n", - "A_float = np.array([a/1000 for a in A]).reshape(n, r)\n", - "B_float = np.array([b/1000 for b in B]).reshape(r, n)\n", - "\n", - "distance = (( (A_float @ B_float) - (W) )**2).sum()\n", - "\n", - "m.minimize(distance)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "ename": "CpoSolverException", - "evalue": "Solver error: Problem size limit exceeded.\nCP Optimizer Community Edition solves problems with search spaces up to 2^1000.\nUnrestricted version options (including academia) at https://ibm.co/2s0wqSa\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mCpoSolverException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlog_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTimeLimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1200\u001b[39;49m\u001b[43m)\u001b[49m \n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# 0.3136016\u001b[39;00m\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/model.py:1289\u001b[0m, in \u001b[0;36mCpoModel.solve\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 1246\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" Solves the model.\u001b[39;00m\n\u001b[1;32m 1247\u001b[0m \n\u001b[1;32m 1248\u001b[0m \u001b[38;5;124;03mThis method solves the model using the appropriate :class:`~docplex.cp.solver.solver.CpoSolver`\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1286\u001b[0m \u001b[38;5;124;03m :class:`~docplex.cp.utils.CpoException`: (or derived) if error.\u001b[39;00m\n\u001b[1;32m 1287\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1288\u001b[0m solver \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_solver(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1289\u001b[0m msol \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1290\u001b[0m solver\u001b[38;5;241m.\u001b[39mend()\n\u001b[1;32m 1291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msol\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/solver/solver.py:708\u001b[0m, in \u001b[0;36mCpoSolver.solve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39mlog_exceptions:\n\u001b[1;32m 707\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n\u001b[0;32m--> 708\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_status(STATUS_IDLE)\n\u001b[1;32m 710\u001b[0m stime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m stime\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/solver/solver.py:701\u001b[0m, in \u001b[0;36mCpoSolver.solve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 699\u001b[0m stime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 701\u001b[0m msol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 703\u001b[0m \u001b[38;5;66;03m# Check if aborted in the mean time\u001b[39;00m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_status_aborted():\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/solver/solver_local.py:213\u001b[0m, in \u001b[0;36mCpoSolverLocal.solve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write_message(CMD_SOLVE_MODEL)\n\u001b[1;32m 212\u001b[0m \u001b[38;5;66;03m# Wait JSON result\u001b[39;00m\n\u001b[0;32m--> 213\u001b[0m jsol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait_json_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mEVT_SOLVE_RESULT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Build result object\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_result_object(CpoSolveResult, jsol)\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/solver/solver_local.py:572\u001b[0m, in \u001b[0;36mCpoSolverLocal._wait_json_result\u001b[0;34m(self, evt)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" Wait for a JSON result while forwarding logs if any.\u001b[39;00m\n\u001b[1;32m 565\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m 566\u001b[0m \u001b[38;5;124;03m evt: Event to wait for\u001b[39;00m\n\u001b[1;32m 567\u001b[0m \u001b[38;5;124;03mReturns:\u001b[39;00m\n\u001b[1;32m 568\u001b[0m \u001b[38;5;124;03m JSON solution string, decoded from UTF8\u001b[39;00m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;66;03m# Wait JSON result\u001b[39;00m\n\u001b[0;32m--> 572\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 574\u001b[0m \u001b[38;5;66;03m# Store last json result\u001b[39;00m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_last_json_result_string(data)\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/solver/solver_local.py:529\u001b[0m, in \u001b[0;36mCpoSolverLocal._wait_event\u001b[0;34m(self, xevt)\u001b[0m\n\u001b[1;32m 527\u001b[0m data \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m firsterror \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend()\n\u001b[0;32m--> 529\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CpoSolverException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSolver error: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m data)\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 532\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend()\n", - "\u001b[0;31mCpoSolverException\u001b[0m: Solver error: Problem size limit exceeded.\nCP Optimizer Community Edition solves problems with search spaces up to 2^1000.\nUnrestricted version options (including academia) at https://ibm.co/2s0wqSa\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output=True, TimeLimit=1200) \n", - "# 0.3136016" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A_solution = []\n", - "B_solution = []\n", - "\n", - "for i in range(n*r):\n", - " A_solution.append(solution[A.tolist()[i]] / 1000)\n", - " B_solution.append(solution[B.tolist()[i]] / 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "W_hat = np.array(A_solution).reshape(n, r) @ np.array(B_solution).reshape(r, n)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'W_hat' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ((\u001b[43mW_hat\u001b[49m \u001b[38;5;241m-\u001b[39m W)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39msum()\n", - "\u001b[0;31mNameError\u001b[0m: name 'W_hat' is not defined" - ] - } - ], - "source": [ - "\n", - "((W_hat - W)**2).sum()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'W_hat' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mW_hat\u001b[49m\n", - "\u001b[0;31mNameError\u001b[0m: name 'W_hat' is not defined" - ] - } - ], - "source": [ - "W_hat" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.548814, 0.715189, 0.602763, ..., 0.414662, 0.264556, 0.774234],\n", - " [0.45615 , 0.568434, 0.01879 , ..., 0.110375, 0.65633 , 0.138183],\n", - " [0.196582, 0.368725, 0.820993, ..., 0.716327, 0.289406, 0.183191],\n", - " ...,\n", - " [0.77311 , 0.21687 , 0.90315 , ..., 0.522176, 0.853606, 0.889448],\n", - " [0.220104, 0.622894, 0.111496, ..., 0.771225, 0.012171, 0.32283 ],\n", - " [0.229567, 0.506863, 0.736853, ..., 0.6205 , 0.639622, 0.94854 ]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Alternate Solution" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/100000 [00:00 39\u001b[0m B_ \u001b[38;5;241m=\u001b[39m [((np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(r, n)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m) \u001b[38;5;241m*\u001b[39m learning_rate) \u001b[38;5;241m+\u001b[39m B[best_index] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(Dupe_count)]\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# print(A_)\u001b[39;00m\n\u001b[1;32m 43\u001b[0m A \u001b[38;5;241m=\u001b[39m A_\n", - "Cell \u001b[0;32mIn[7], line 39\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 36\u001b[0m best_index \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmin(distances)\n\u001b[1;32m 38\u001b[0m A_ \u001b[38;5;241m=\u001b[39m [((np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(n, r)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m) \u001b[38;5;241m*\u001b[39m learning_rate) \u001b[38;5;241m+\u001b[39m A[best_index] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(Dupe_count)]\n\u001b[0;32m---> 39\u001b[0m B_ \u001b[38;5;241m=\u001b[39m [((\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrand\u001b[49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m) \u001b[38;5;241m*\u001b[39m learning_rate) \u001b[38;5;241m+\u001b[39m B[best_index] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(Dupe_count)]\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# print(A_)\u001b[39;00m\n\u001b[1;32m 43\u001b[0m A \u001b[38;5;241m=\u001b[39m A_\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "np.random.seed(0)\n", - "from tqdm import tqdm\n", - "\n", - "n = 64\n", - "r = 32\n", - "# myparams = CpoParameters()\n", - "m = Model()\n", - "\n", - "W = np.random.rand(n, n)\n", - "Dupe_count = 32\n", - "learning_rate = 0.001\n", - "EPOCHS = 100000\n", - "\n", - "all_best_distances = [10e8]\n", - "\n", - "A = [np.random.rand(n, r) for _ in range(Dupe_count)]\n", - "B = [np.random.rand(r, n) for _ in range(Dupe_count)]\n", - "\n", - "for i in tqdm(range(EPOCHS)):\n", - "\n", - " possibles = []\n", - "\n", - " for a, b in zip(A, B):\n", - " possibles.append(a@b)\n", - "\n", - " distances =[]\n", - " for idx, poss in enumerate(possibles):\n", - " distances.append(( ((poss) - (W) )**2).sum())\n", - "\n", - "\n", - " if np.min(distances) >= np.min(all_best_distances):\n", - " continue\n", - "\n", - " all_best_distances.append(np.min(distances))\n", - "\n", - " best_index = np.argmin(distances)\n", - "\n", - " A_ = [((np.random.rand(n, r)-0.5) * learning_rate) + A[best_index] for _ in range(Dupe_count)]\n", - " B_ = [((np.random.rand(r, n)-0.5) * learning_rate) + B[best_index] for _ in range(Dupe_count)]\n", - "\n", - " # print(A_)\n", - "\n", - " A = A_\n", - " B = B_\n", - "\n", - " lowest_distance = np.min(distances)\n", - "\n", - " if i % 1000 == 0:\n", - " print(f'Reached Epoch {i} with distance {lowest_distance}')\n", - " learning_rate -= 0.00001" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.plot(all_best_distances[1:])" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "205.49650823550252" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_best_distances[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,\n", - " 0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152,\n", - " 0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606,\n", - " 0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215,\n", - " 0.97861834, 0.79915856, 0.46147936, 0.78052918, 0.11827443,\n", - " 0.63992102, 0.14335329, 0.94466892, 0.52184832, 0.41466194,\n", - " 0.26455561, 0.77423369, 0.45615033, 0.56843395, 0.0187898 ,\n", - " 0.6176355 , 0.61209572, 0.616934 , 0.94374808, 0.6818203 ,\n", - " 0.3595079 , 0.43703195, 0.6976312 , 0.06022547, 0.66676672,\n", - " 0.67063787, 0.21038256, 0.1289263 , 0.31542835, 0.36371077,\n", - " 0.57019677, 0.43860151, 0.98837384, 0.10204481, 0.20887676,\n", - " 0.16130952, 0.65310833, 0.2532916 , 0.46631077, 0.24442559,\n", - " 0.15896958, 0.11037514, 0.65632959, 0.13818295]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W[:1]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.44256429, 0.53827876, 0.47480137, 0.61519475, 0.58260742,\n", - " 0.56843893, 0.45556814, 0.64610971, 0.77281468, 0.39536792,\n", - " 0.51052557, 0.41572843, 0.5553761 , 0.83698542, 0.16370298,\n", - " 0.44696992, 0.21374778, 0.74687941, 0.34679602, 0.58910765,\n", - " 0.56707936, 0.50925248, 0.50661505, 0.66326411, 0.05034912,\n", - " 0.71679844, 0.41421399, 0.52798474, 0.75863155, 0.57991776,\n", - " 0.30463705, 0.56892616, 0.44259512, 0.41057791, 0.32003543,\n", - " 0.46886895, 0.4089395 , 0.46595273, 0.66987709, 0.53688839,\n", - " 0.49296935, 0.64164668, 0.65569185, 0.35627781, 0.48744864,\n", - " 0.52200075, 0.54076961, 0.18914343, 0.51702804, 0.41517277,\n", - " 0.39479315, 0.55722324, 0.85716223, 0.18230271, 0.3141962 ,\n", - " 0.06932318, 0.68103497, 0.41798767, 0.63270966, 0.80774156,\n", - " 0.32853918, 0.54547272, 0.91917801, 0.37671129]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(A[best_index] @ B[best_index])[:1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(0)\n", - "from tqdm import tqdm\n", - "\n", - "n = 64\n", - "r = 32\n", - "# myparams = CpoParameters()\n", - "m = Model()\n", - "\n", - "W = np.random.rand(n, n)\n", - "Dupe_count = 32\n", - "learning_rate = 0.001\n", - "EPOCHS = 100000\n", - "\n", - "all_best_distances = [10e8]\n", - "\n", - "A = [np.random.rand(n, r) for _ in range(Dupe_count)]\n", - "B = [np.random.rand(r, n) for _ in range(Dupe_count)]\n", - "\n", - "for i in tqdm(range(EPOCHS)):\n", - "\n", - " possibles = []\n", - "\n", - " for a, b in zip(A, B):\n", - " possibles.append(a@b)\n", - "\n", - " distances =[]\n", - " for idx, poss in enumerate(possibles):\n", - " distances.append(( ((poss) - (W) )**2).sum())\n", - "\n", - "\n", - " if np.min(distances) >= np.min(all_best_distances):\n", - " continue\n", - "\n", - " all_best_distances.append(np.min(distances))\n", - "\n", - " best_index = np.argmin(distances)\n", - "\n", - " A_ = [((np.random.rand(n, r)-0.5) * learning_rate) + A[best_index] for _ in range(Dupe_count)]\n", - " B_ = [((np.random.rand(r, n)-0.5) * learning_rate) + B[best_index] for _ in range(Dupe_count)]\n", - "\n", - " # print(A_)\n", - "\n", - " A = A_\n", - " B = B_\n", - "\n", - " lowest_distance = np.min(distances)\n", - "\n", - " if i % 1000 == 0:\n", - " print(f'Reached Epoch {i} with distance {lowest_distance}')\n", - " learning_rate -= 0.00001" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Binary Variables/BankLocation_Problem.ipynb b/Binary Variables/BankLocation_Problem.ipynb deleted file mode 100644 index 8ead692..0000000 --- a/Binary Variables/BankLocation_Problem.ipynb +++ /dev/null @@ -1,68 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The long-range planning department for the Ohio Trust Company is considering expanding\n", - "its operation into a 20-county region in northeastern Ohio (Figure 13.10). Currently, Ohio\n", - "Trust does not have a principal place of business in any of the 20 counties. According to the\n", - "banking laws in Ohio, if a bank establishes a principal place of business (PPB) in any county,\n", - "branch banks can be established in that county and in any of the adjacent counties. However,\n", - "to establish a new principal place of business, Ohio Trust must either obtain approval for a\n", - "new bank from the state’s superintendent of banks or purchase an existing bank." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets and Indicies\n", - "\n", - "$\\mathcal{L}$: All banks locations
\n", - "$l, l \\in \\mathcal{L}$: An index represesnting a bank location\n", - "\n", - "#### Data\n", - "l_1 = neigbors of l_1\n", - "l_2 = neigbors of l_2\n", - "l_3 = neigbors of l_3\n", - "...\n", - "\n", - "#### D.V\n", - "x for each x in L\n", - "\n", - "#### Function\n", - "Minimize sum of x\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Binary Variables/PartsWorth_Problem.ipynb b/Binary Variables/PartsWorth_Problem.ipynb deleted file mode 100644 index 549a7d5..0000000 --- a/Binary Variables/PartsWorth_Problem.ipynb +++ /dev/null @@ -1,39 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We represent the problem by \n", - "[\n", - " [0, 0, 0, 0], # Thin Crust, Mozerella Cheese, Smooth Sauce, Mild Sausage\n", - " [0, 0, 0, 0], # Thick Crust, Mixed Cheese, Chunky Sauce, Medium Sausage\n", - " [-, -, -, 0] # - , - , - , Hot Sausage\n", - "]\n", - "\n", - "How to know if customer 1 will buy put pizza?\n", - " 11L_{11} + 2L_{21} + 6L_{12} + ... >= 51\n", - " \n", - " 1 is already enforced, because it is trying to maximize.\n", - " 0 Needs to be enforced. \n", - "\n", - " 11L_{11} + 2L_{21} + 6L_{12} + ... >= (51 * Y_1) + 1\n", - "\n", - "\n", - "\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Binary Variables/notes.txt b/Binary Variables/notes.txt deleted file mode 100644 index 45e8fa5..0000000 --- a/Binary Variables/notes.txt +++ /dev/null @@ -1,29 +0,0 @@ -Fixed Cost -> Doesn't take volume into consideration, AKA only added once - -Let's say we have the continuous variable x, -To represent whether the variable x was used whatsoever, we - -xx = 1_{{x > 0}} - -such that : - -1_{{.}} = { 1, if . is satisfied - { 0, otherwise - -And if x had a Fixed Cost (A) which was applied once, e.g. setup costs which is only - - applied. We can do as Follow - -Maximize {(PROFIT * x) - (A * xx)} - -Auxiliary DV: Their values aren't independant, and they add no new info, - - and thier values rely on other variables, also, they are used to write - - a simplify the formulation - -Examples on Auxiliary DV: - - F is a D.V. and F has a fixed cost of M. - - F <= [MaxCreationLimitForF] * FF. - - FF is the Auxiliary DV. - - - - \ No newline at end of file diff --git a/Decision Analysis/CarLeases_Problem.ipynb b/Decision Analysis/CarLeases_Problem.ipynb deleted file mode 100644 index fa08472..0000000 --- a/Decision Analysis/CarLeases_Problem.ipynb +++ /dev/null @@ -1,218 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Amy Lloyd is interested in leasing a new Honda and has contacted three\n", - "automobile dealers for pricing information. Each dealer offered Amy a closed-end\n", - "36-month lease with no down payment due at the time of signing. Each lease includes a\n", - "monthly charge and a mileage allowance. Additional miles receive a surcharge on a per-\n", - "mile basis. The monthly lease cost, the mileage allowance, and the cost for additional\n", - "miles are as follows:\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DealerMonthly CostMileage AllowanceCost per Additional Mile
Hepburn Honda$29936,000$0.15
Midtown Motors$31045,000$0.20
Hopkins Automotive$32554,000$0.15
\n", - "\n", - "\n", - "Amy decided to choose the lease option that will minimize her total 36-month cost. The difficulty is that Amy is not sure how many miles she will drive over the next three years. For purposes of this decision, she believes it is reasonable to assume that she will drive 12,000 miles per year, 15,000 miles per year, or 18,000 miles per year. With this assumption Amy estimated her total costs for the three lease options. For example, she figures that the Hepburn Honda lease will cost her

\n", - "$36($299) + $0.15(36,000 - 36,000) = $10,764$ if she drives 12,000 miles per year,
\n", - "$36($299) + $0.15(45,000 - 36,000) = $12,114$ if she drives 15,000 miles per year,
\n", - "$36($299) + $0.15(54,000 - 36,000) = $13,464$ if she drives 18,000 miles per year.
\n", - "\n", - "\n", - "a. What is the decision, and what is the chance event?
\n", - "b. Construct a payoff table for Amy’s problem.
\n", - "c. If Amy has no idea which of the three mileage assumptions is most appropriate, what is the recommended decision (leasing option) using the optimistic, conservative, and minimax regret approaches?
\n", - "d. Suppose that the probabilities that Amy drives 12,000, 15,000, and 18,000 miles per year are 0.5, 0.4, and 0.1, respectively. What option should Amy choose using the expected value approach?
\n", - "e. Develop a risk profile for the decision selected in part (d). What is the most likely cost, and what is its probability?
\n", - "f. Suppose that, after further consideration, Amy concludes that the probabilities\n", - "that she will drive 12,000, 15,000, and 18,000 miles per year are 0.3, 0.4, and 0.3,\n", - "respectively. What decision should Amy make using the expected value approach?\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11160.0\n", - "11160.0\n", - "12960.0\n", - "11700.0\n", - "11700.0\n", - "11700.0\n" - ] - } - ], - "source": [ - "##### B ######\n", - "print((36 * 310) + (0.2 * (max(0, ((3 * 12_000) - 45_000)))))\n", - "print((36 * 310) + (0.2 * (max(0, ((3 * 15_000) - 45_000)))))\n", - "print((36 * 310) + (0.2 * (max(0, ((3 * 18_000) - 45_000)))))\n", - "# ---------------\n", - "print((36 * 325) + (0.15 * (max(0, ((3 * 12_000) - 54_000)))))\n", - "print((36 * 325) + (0.15 * (max(0, ((3 * 15_000) - 54_000)))))\n", - "print((36 * 325) + (0.15 * (max(0, ((3 * 18_000) - 54_000)))))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solution:\n", - "a. Chance event is How many miles Amy will drive.
\n", - "______\n", - "b. \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DealerCost of 12000 milesCost of 15000 milesCost of 18000 miles
Hepburn Honda10,76412,11413,464
Midtown Motors11,16011,16012,960
Hopkins Automotive11,70011,70011,700
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_____________\n", - "c.
\n", - "Optimistic (12,000 miles): Hepburn Honda - 10,764
\n", - "Conservative (18,000 miles): Hopkins Automotive - 11,700
\n", - "Minimax: \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DealerCost of 12000 milesCost of 15000 milesCost of 18000 miles
Hepburn Honda09541260
Midtown Motors39601764
Hopkins Automotive9365400
\n", - "\n", - "Meaning Hopkins Automotive is the best option with 1476." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______________\n", - "d.
\n", - "Hupburn = $0.5(10746) + 0.4(12114) + 0.1(13464) = 11565$
\n", - "Midtown = $0.5(11160) + 0.4(11160) + 0.1(12960) = 11340$
\n", - "Hopkins = $0.5(11700) + 0.4(11700) + 0.1(11700) = 11700$
\n", - "\n", - "meaning Midtown motors would be the best choice" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "____________\n", - "e.

\n", - "\n", - "\n", - "11,160 would be the likely with 50%.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "f. Decide on something Amy" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/CarpoolRoute_Problem.ipynb b/Decision Analysis/CarpoolRoute_Problem.ipynb deleted file mode 100644 index e69de29..0000000 diff --git a/Decision Analysis/ChardonnayOrRiesling_Problem.ipynb b/Decision Analysis/ChardonnayOrRiesling_Problem.ipynb deleted file mode 100644 index 1fbc0ac..0000000 --- a/Decision Analysis/ChardonnayOrRiesling_Problem.ipynb +++ /dev/null @@ -1,121 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Seneca Hill Winery recently purchased land for the\n", - "purpose of establishing a new vineyard. Management is considering two varieties of white\n", - "grapes for the new vineyard: Chardonnay and Riesling. The Chardonnay grapes would be\n", - "used to produce a dry Chardonnay wine, and the Riesling grapes would be used to produce a semidry Riesling wine. It takes approximately four years from the time of planting\n", - "before new grapes can be harvested. This length of time creates a great deal of uncertainty\n", - "concerning future demand and makes the decision about the type of grapes to plant difficult. Three possibilities are being considered: Chardonnay grapes only; Riesling grapes\n", - "only; and both Chardonnay and Riesling grapes. Seneca management decided that for\n", - "planning purposes it would be adequate to consider only two demand possibilities for each\n", - "type of wine: strong or weak. With two possibilities for each type of wine, it was necessary to assess four probabilities. With the help of some forecasts in industry publications,\n", - "management made the following probability assessments:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Riesling Demand
Chardonnay DemandWeakStrong
Weak0.050.50
Strong0.250.20
\n", - "\n", - "\n", - "Revenue projections show an annual contribution to profit of $20,000 if Seneca Hill plants\n", - "only Chardonnay grapes and demand is weak for Chardonnay wine, and $70,000 if ­Seneca\n", - "plants only Chardonnay grapes and demand is strong for Chardonnay wine. If ­Seneca plants\n", - "only Riesling grapes, the annual profit projection is $25,000 if demand is weak for Riesling\n", - "grapes and $45,000 if demand is strong for Riesling grapes. If Seneca plants both types of\n", - "grapes, the annual profit projections are as shown in the following table:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Riesling Demand
Chardonnay DemandWeakStrong
Weak$22,000$40,000
Strong$26,000$60,000
\n", - "\n", - "a. What is the decision to be made, what is the chance event, and what is the consequence? Identify the alternatives for the decisions and the possible outcomes for the\n", - "chance events.
\n", - "b. Develop a decision tree.
\n", - "c. Use the expected value approach to recommend which alternative Seneca Hill\n", - "­Winery should follow in order to maximize expected annual profit.
\n", - "d. Suppose management is concerned about the probability assessments when demand\n", - "for Chardonnay wine is strong. Some believe it is likely for Riesling demand\n", - "to also be strong in this case. Suppose that the probability of strong demand for\n", - "­Chardonnay and weak demand for Riesling is 0.05 and that the probability of strong\n", - "demand for Chardonnay and strong demand for Riesling is 0.40. How does this\n", - "change the recommended decision? Assume that the probabilities when Chardonnay\n", - "demand is weak are still 0.05 and 0.50.
\n", - "e. Other members of the management team expect the Chardonnay market to become\n", - "saturated at some point in the future, causing a fall in prices. Suppose that the\n", - "annual profit projections fall to $50,000 when demand for Chardonnay is strong and\n", - "only Chardonnay grapes are planted. Using the original probability assessments,\n", - "determine how this change would affect the optimal decision.
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solution\n", - "a. Whether to plan Chardonnay or Riesling. Whether the demand for either of the plants is going to be weak of strong.\n", - "______________________\n", - "b. \n", - "\n", - "_____________________\n", - "c. \n", - "\n", - "_____________________\n", - "d. \n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/ClevelandToMyrtle_Problem.ipynb b/Decision Analysis/ClevelandToMyrtle_Problem.ipynb deleted file mode 100644 index 93bfab0..0000000 --- a/Decision Analysis/ClevelandToMyrtle_Problem.ipynb +++ /dev/null @@ -1,192 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Myrtle Air Express decided to offer direct service from Cleveland to Myrtle Beach. Management must decide between a full-price service using the company’s new fleet of jet aircraft and a discount service using smaller-capacity commuter planes. It is clear that the best choice depends on the market reaction to the service Myrtle Air offers. Management developed estimates of the contribution to profit for each type of service based on two possible levels of demand for service to Myrtle Beach: strong and weak. The following table shows the estimated quarterly profits (in thousands of dollars):\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ServiceStrongWeak
Full price$960-$490
Discount$670$320
\n", - "\n", - "a. What is the decision to be made, what is the chance event, and what is the consequence for this problem? How many decision alternatives are there? How many\n", - "outcomes are there for the chance event?

\n", - "b. If nothing is known about the probabilities of the chance outcomes, what is the\n", - "recommended decision using the optimistic, conservative, and minimax regret\n", - "approaches?

\n", - "c. Suppose that management of Myrtle Air Express believes that the probability of\n", - "strong demand is 0.7 and the probability of weak demand is 0.3. Use the expected\n", - "value approach to determine an optimal decision.

\n", - "d. Suppose that the probability of strong demand is 0.8 and the probability of\n", - "weak demand is 0.2. What is the optimal decision using the expected value\n", - "approach?

\n", - "e. Use sensitivity analysis to determine the range of demand probabilities for which\n", - "each of the decision alternatives has the largest expected value." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solution\n", - "\n", - "a. ---\n", - "\n", - "____________\n", - "\n", - "b. \n", - "Optimistic = Strong if Full Price: $960
\n", - "Conservative = Weak if Discount: $320
\n", - "Minimax = \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ServiceStrongWeak
Full price0810
Discount2900
\n", - "\n", - "We choose discount because the regret is lower = $290\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__________\n", - "\n", - "c. \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ServiceStrongWeak
Full price672-$147
Discount46996
\n", - "\n", - "Full Price = 672 - 147 = 525
\n", - "Discount = 469 + 96 = 565
\n", - "\n", - "Meaning Discount is better = 565 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_________________________\n", - "d.\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ServiceStrongWeak
Full price768-98
Discount53664
\n", - "\n", - "Full Price = 768 - 98 = 670
\n", - "Discount = 536 + 64 = 600
\n", - "Meaning that Full price is better = 670\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_________________________________" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Find intersection between the two equations:\n", - "\n", - "$Full Price = x960 - (1-x)490$
\n", - "$Discount = x670 + (1-x)320$
\n", - "\n", - "$x670 + (1-x)320 = x960 - (1-x)490$
\n", - "\n", - "$x670 + 320-x320 = x960 - 490 + x490$
\n", - "\n", - "$x350 + 320 = x1450 -490$
\n", - "\n", - "$810 = x1100$
\n", - "\n", - "$x = \\frac{81}{110}$
\n", - "\n", - "$x = 0.736363636$
\n", - "\n", - "Meaning that any value higher than x would mean that higher price is better, and lower than X means that Discount is better." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/DataWarehouse_Problem.ipynb b/Decision Analysis/DataWarehouse_Problem.ipynb deleted file mode 100644 index e4d33ef..0000000 --- a/Decision Analysis/DataWarehouse_Problem.ipynb +++ /dev/null @@ -1,96 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hudson Corporation is considering three options for managing its data warehouse: continuing with its own staff, hiring an outside vendor to do the managing, or using a combination of its own staff and an outside vendor. The cost of the operation depends on future demand. The annual cost of each option (in thousands of dollars) depends on demand as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Staffing OptionsHighMediumLow
Own staff650650600
Outside vendor900600300
Combination800650500
\n", - "\n", - "a. If the demand probabilities are 0.2, 0.5, and 0.3, which decision alternative will minimize the expected cost of the data warehouse? What is the expected annual cost associated with that recommendation?

\n", - "b. Construct a risk profile for the optimal decision in part (a). What is the probability of the cost exceeding $700,000?
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "a. \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Staffing OptionsHighMediumLow
Own staff130325180
Outside vendor18030090
Combination160325150
\n", - "\n", - "Own Staff = 130 + 325 + 180 = 635\n", - "Outside Vendor = 180 + 300 + 90 = 570\n", - "Combination = 160 + 325 + 150 = 635\n", - "\n", - "Outside Vendor would be the best. = 570\n", - "\n", - "______________\n", - "\n", - "\n", - "b. \n", - "\n", - "\n", - "The chance is 20%" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/ImagesForSolutions/DecisionTree_Winery.png b/Decision Analysis/ImagesForSolutions/DecisionTree_Winery.png deleted file mode 100644 index 1d3fac9bf0040e560c742bfb96c1606d8a782776..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 122305 zcmbrm1yGjX);3NlARW>mjUXW?E!{03(k0R<-6<(ZgLHRymmneC9ZGk1_}|Y@IPW>< z{bs)Z%s2DsILdS1d#}CrTGzVPwG4bKD~9rf;0Y8I6pDnnumThmED!i~gYX#qjoIAL zM<^&#C<$SKcaB;+N$`$OMb10Xl!WVT-$G|S8F>Vk2`3myu4E#Jq8Vgc3LUNm11BgW zXqk3SEgWZa>~^A*I1`RTYtvx~K+^+bn=#-aGq za45vEfB$$UV2TS4FK55#HU6)EdJcol$jj 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zha;`S_kX5y1{tg!t!xU80q{gV#H2}>)c|u-$0Nm>SC0%G#$rr|7rICSio;H7JavpA zyL^JkttdZmtMO{)mrE@$59Fv$;(rnf_tF8{m{z$GsRU?`&rXuTa~LO)ygkQ(jby~U zGf>WqCgcYnp9dEz=lcWyQJuftPTIBle3+i2MP(&AH`rCb%%t zxhJdeGrJCbatAj|7;5?Vp>hAb%ti;?vltzGQTVf2{T7S_V=z_{F+D#=`*rJ$xT7CB zN5b`frf1871-M=!Gjo%EA`=Y6F$8yB(VHDyezGtyssRVCYmgJ}i$5l&o diff --git a/Decision Analysis/MarketSegment_Problem.ipynb b/Decision Analysis/MarketSegment_Problem.ipynb deleted file mode 100644 index eabff0d..0000000 --- a/Decision Analysis/MarketSegment_Problem.ipynb +++ /dev/null @@ -1,148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Investment advisors estimated the stock market returns for four market segments: computers, financial, manufacturing, and pharmaceuticals. Annual return projections vary depending on whether the general economic conditions are improving, stable, or declining. The anticipated annual return percentages for each market segment under each economic condition are as follows:\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Market SegmentImprovingStableDeclining
Computers102-4
Financial85-3
Manufacturing64-2
Pharmaceuticals65-1
\n", - "\n", - "a. Assume that an individual investor wants to select one market segment for a new investment. A forecast shows improving to declining economic conditions with the following probabilities: improving (0.2), stable (0.5), and declining (0.3). What is the preferred market segment for the investor, and what is the expected return percentage?

\n", - "b. At a later date, a revised forecast shows a potential for an improvement in economic conditions. New probabilities are as follows: improving (0.4), stable (0.4), and declining (0.2). What is the preferred market segment for the investor based on these new probabilities? What is the expected return percentage?
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solution\n", - "\n", - "a.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Market SegmentImprovingStableDeclining
Computers21-1.2
Financial1.62.5-0.9
Manufacturing1.22-0.6
Pharmaceuticals1.22.5-0.3
\n", - "\n", - "Computers = 2 + 1 - 1.2 = 1.8
\n", - "Financial = 1.6 + 2.5 - 0.9 = 3
\n", - "Manufacturing = 1.2 + 2 - 0.6 = 2.6
\n", - "Pharma = 1.2 + 2.5 - 0.3 = 3.4
\n", - "\n", - "Pharma is the best option = 3.4\n", - "\n", - "________________\n", - "\n", - "b.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Market SegmentImprovingStableDeclining
Computers40.8-0.8
Financial3.22-0.6
Manufacturing2.41.6-0.4
Pharmaceuticals2.42-0.2
\n", - "\n", - "Computers = 4
\n", - "Financial = 4.6
\n", - "Manufacturing = 3.6
\n", - "Pharma = 4.2
\n", - "\n", - "Financial is the best option = 4.6" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/PlantSize_Problem.ipynb b/Decision Analysis/PlantSize_Problem.ipynb deleted file mode 100644 index 03abc02..0000000 --- a/Decision Analysis/PlantSize_Problem.ipynb +++ /dev/null @@ -1,93 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Southland Corporation’s decision to produce a new line of recreational products resulted in the need to construct either a small plant or a large plant. The best selection of plant size depends on how the marketplace reacts to the new product line.
\n", - "To conduct an analysis, marketing management has decided to view the possible long-run\n", - "demand as low, medium, or high. The following payoff table shows the projected profit in\n", - "millions of dollars:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Plan SizeLowMediumHigh
Small150200200
Large50200500
\n", - "\n", - "a. What is the decision to be made, and what is the chance event for Southland’s problem?
\n", - "b. Construct a decision tree.
\n", - "c. Recommend a decision based on the use of the optimistic, conservative, and mini-max regret approaches.
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solution\n", - "\n", - "a. Chance events are the Demands, which are [Low, Medium, High]
\n", - "_____\n", - "b.

\n", - "_____\n", - "c.
\n", - "Optimistic: 500M
\n", - "Conservative: 150M
\n", - "mini-max: \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Plan SizeLowMediumHigh
Small00300
Large10000
\n", - "\n", - "Meaning that 100M." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/RezoningProperty_Problem.ipynb b/Decision Analysis/RezoningProperty_Problem.ipynb deleted file mode 100644 index e69de29..0000000 diff --git a/Decision Analysis/TwoStates_Problem.ipynb b/Decision Analysis/TwoStates_Problem.ipynb deleted file mode 100644 index 44c25d5..0000000 --- a/Decision Analysis/TwoStates_Problem.ipynb +++ /dev/null @@ -1,76 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following payoff table shows the profit for a decision problem with two states of nature and two decision alternatives:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Decision Alternatives1s2
d1101
d243
\n", - "\n", - "a. Suppose P(s1) = 0.2 and P(s2) = 0.8. What is the best decision using the expected value approach?

\n", - "b. Perform sensitivity analysis on the payoffs for decision alternative d1. Assume that the probabilities are as given in part (a), and find the range of payoffs under states of nature s1 and s2 that will keep the solution found in part (a) optimal. Is the solution more sensitive to the payoff under state of nature s1 or s2 ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Solution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "a.\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Decision Alternatives1s2
d120.8
d20.82.4
\n", - "\n", - "Decision 2 is better, at 3.2 profit\n", - "\n", - "____________\n", - "\n", - "b. idk if we took that" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/VideoGameProfitability_Problem.ipynb b/Decision Analysis/VideoGameProfitability_Problem.ipynb deleted file mode 100644 index 071aabc..0000000 --- a/Decision Analysis/VideoGameProfitability_Problem.ipynb +++ /dev/null @@ -1,127 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Video Tech is considering marketing one of two new video\n", - "games for the coming holiday season: Battle Pacific or Space Pirates. Battle Pacific is\n", - "a unique game and appears to have no competition. Estimated profits (in thousands of\n", - "dollars) under high, medium, and low demand are as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Battle PacificHighDMediumLow
Profit$1,000$700$300
Probability0.20.50.3
\n", - "\n", - "Video Tech is optimistic about its Space Pirates game. However, the concern is that\n", - "profitability will be affected by a competitor’s introduction of a video game viewed as\n", - "similar to Space Pirates. Estimated profits (in thousands of dollars) with and without\n", - "competition are as follows: \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Space Pirates With CompetitionHighMediumLow
Profit$800$400$200
Probability0.30.40.3
\n", - "\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Space Pirates Without CompetitionHighMediumLow
Profit$1,600$800$400
Probability0.50.30.2
\n", - "\n", - "Develop a decision tree for the Video Tech problem.\n", - "b. For planning purposes, Video Tech believes there is a 0.6 probability that its com-\n", - "petitor will produce a new game similar to Space Pirates. Given this probability\n", - "of competition, the director of planning recommends marketing the Battle Pacific\n", - "video game. Using expected value, what is your recommended decision?\n", - "c. Show a risk profile for your recommended decision.\n", - "d. Use sensitivity analysis to determine what the probability of competition for Space\n", - "Pirates would have to be for you to change your recommended decision alternative." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\"Image\n", - "\n", - "\n", - "\"Image\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Decision Analysis/notes.md b/Decision Analysis/notes.md deleted file mode 100644 index 82074ea..0000000 --- a/Decision Analysis/notes.md +++ /dev/null @@ -1,199 +0,0 @@ -# Decision Analysis (Without Proba) -## Example: Pittsburhg development -* Plan to build one of these three projects: - * One project with 30 condos - * One project with 60 condos - * One project with 90 condos -* Future event is the chance event concerning the demand for the condos. -* The financial success depends on the condos size -* To solve this, one should do one of the following methods: -### Payoff Tables -1) Decision alternatives (How many condos?) -2) Chance event (States of nature, i.e. Weak demand, or Strong Demand) -3) You then create a table with the columns being States of nature, and the rows being the decision alternatives. Like the table below. -4) Then based on the tables, you can apply 3 different options. - * Minimax Regret ; Regret = (a_max - a_i).
- For example,
Strong Demand, and Small Condo, will have a regret of 20 - 8 = 12.
Weak Demand, and Medium Condo, 7 - 5 = 2 - * Optimistic: Takes the highest one from the Strong demand. - * Conservative: Takes the highest one from the Weak demand. - - - - - - - - - - - - - - - - - - - - - - - - - - -
Decision AlternatievsStrong DemandWeak Demand
Small Condos $d_1$87
Medium Condos d2145
Large Condos d320-9
- - -### Decision Tree -1) Decision alternatives (How many condos?) -2) Chance event (States of nature, i.e. Weak demand, or Strong Demand) -3) Create a tree, where (Check slides lol, I will not be bothered to write the tree here.) - -# Decision Analysis with Probs: - * Expected value: If you had the probabilities for each event to occur, then you mutliple the chance of X occuring by the "profit" if X occured. - * Risk: Probability mass function, for example: - * 8 mil, if Event 1 occured - * 2 mil, if Event 2 occured - * -4 mil, if Event 3 occured - - * Sensitivity Analysis: How much change is required for the optimal solution to stay optimal. - - -# Decision Analysis with Sample Information: -## Example: PDC Management -* Decision Strategy - Metric 1: EVSI -> Expected Value of Sample Information - * Done by reducing the branches over and over \sum(prob * value). - * ***In this example, we took the maxes. -* Decision Strategy - Metric 21: EVPI -> Expected Value of Perfect Information - * If you will what probability will hit. - * Done by 0.8 * 20 + 0.2 * 7. We got this by knowing that if High demand occurs (80%) we will get large complex (20 mil), and if Low demand occurs (20%) we will get the small complex (7 mil), - -# Computing Brach Probability with Bayes' Theorom -* Join prob: prob that A & B happens at the same time (AND): $P(A \cap B)$ This equals $P(B) P(A|B) = P(A) P(B|A)$ - * If A and B are independant, then $P(A \cap B) = P(A) P(B)$ -* Join prob: prob that A or B happens at the same time (OR): $P(A \cup B)$. This equals $P(A) + P(B) - P(A \cap B)$ -* Conditional Probability: Prob of A given B. $P(A | B)$. This equals $\frac{P(A \cap B)}{P(B)} $ - * If A and B are independant, then $P(A | B) = P(A)$ - -* Bayes Theorem: $P(A|B) = \frac{P(B|A) P(A)}{P(B)}$ -* Bayes Theorem Final Form: $P(B_i|A) = \frac{P(A|B_i) P(B_i)}{\sum_{i=1}P(A|B_i) P(B_i)}$ - * *Assuming the all i in A are independent. - -# Utility Theory -* Utility table is calculated off the pay-off table -* It allows the same problem to get more solutions based on the decision taker, risky or not e.g. -* $Indifference = ???$ -* $Utility = Indifference_{current}Indifference_{max} + Indifference_{current}Indifference_{min}$ - -## Real estate example: -Person A is a risk avoider. -Person B is a risk taker. - -Payoff Table: - - - - - - - - - - - - - - - - - - - - - - - - - -
Investment dxPrice Up s1Price stable s2Price Down s3
Investment A d130k20k-50k
Investment B d250k-20k-30k
Do not invest d3d300
- -Calculating Utility of PayOff table: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Monetary valueIndifference value of pUtility
50kdoes not apply10.0
30k0.959.5
20k0.909
00.757.5
-20k0.555.5
-30k0.44.0
-50kdoes not apply0
- -Replace the utility for each item in the payoff-table the quesiton. - - - - - - - - - - - - - - - - - - - - - - - - - -
Decision Alternativeprices upprices stableprices down
A d19.590
B d2105.54
Do not invest7.57.57.5
- - -## You can express utility by $U(x) = 1 - e^{\frac{-x}{R}}$ \ No newline at end of file diff --git a/Integer Linear Optimization Models/BankTeller_Problem.ipynb b/Integer Linear Optimization Models/BankTeller_Problem.ipynb deleted file mode 100644 index ca1754b..0000000 --- a/Integer Linear Optimization Models/BankTeller_Problem.ipynb +++ /dev/null @@ -1,145 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Northside Bank is working to develop an efficient work\n", - "schedule for full-time and part-time tellers. The schedule must provide for efficient\n", - "operation of the bank, including adequate customer service, employee breaks, and so\n", - "on. On Fridays, the bank is open from 9:00 a.m. to 7:00 p.m. The number of tellers\n", - "necessary to provide adequate customer service during each hour of operation is summarized as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
TimeNo. of Tellers
9:00 a.m. – 10:00 a.m.6
10:00 a.m. – 11:00 a.m.4
11:00 a.m. – Noon8
Noon – 1:00 p.m.10
1:00 p.m. – 2:00 p.m.9
2:00 p.m. – 3:00 p.m.6
3:00 p.m. – 4:00 p.m.4
4:00 p.m. – 5:00 p.m.7
5:00 p.m. – 6:00 p.m.6
6:00 p.m. – 7:00 p.m.6
\n", - "\n", - "Each full-time employee starts on the hour and works a 4-hour shift, followed by a\n", - "1-hour break and then a 3-hour shift. Part-time employees work one 4-hour shift beginning on the hour. Considering salary and fringe benefits, full-time employees cost the\n", - "bank $15 per hour ($105 a day), and part-time employees cost the bank $8 per hour\n", - "($32 per day).\n", - "\n", - "Formulate an integer programming model that can be used to develop a schedule\n", - "that will satisfy customer service needs at a minimum employee cost.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# UNSOLVED" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets and Indicies\n", - "\n", - "$\\mathcal{T}$: A set of working times per day. where each element is represented as $t$. |$\\mathcal{T}$| = $\\textbf{T}$
\n", - "\n", - "#### Data\n", - "$N$: Column Vector of the Number of tellers required per hour, where each element is represented as $n_t, t\\in\\mathcal{T}$.
\n", - "$f$: A number that represents the pay per hour for $\\textbf{Full time}$ employees
\n", - "$p$: A number that represents the pay per hour for $\\textbf{Part time}$ employees
\n", - "\n", - "#### Decision Variable\n", - "\n", - "$W$: A column vector representing the hours being worked for each time $\\in \\mathcal{T}$. Where each element is represented as $w_t$\n", - "\n", - "$x$: Number of $\\textbf{Full time}$ employees
\n", - "$y$: Number of $\\textbf{Part time}$ employees
\n", - "\n", - "#### Function\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{minimize }} \\; {xf + yp} \\\\\n", - "\\ \\text{subject to} \\\\\n", - "\\ \n", - "\\end{align*}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n", - "W_t + W_t1 + W_t2 + W_t3 + W_t5 + W_t6 + W_t7 >= X_t\n", - "W_t + W_t1 + W_t2 + W_t3 >= Y_t" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/CloudServices_Problem.ipynb b/Integer Linear Optimization Models/CloudServices_Problem.ipynb deleted file mode 100644 index 799f7aa..0000000 --- a/Integer Linear Optimization Models/CloudServices_Problem.ipynb +++ /dev/null @@ -1,215 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Galaxy Cloud Services operates several data\n", - "centers across the United States containing servers that store and process the data on\n", - "the Internet. Suppose that Galaxy Cloud Services currently has five outdated data centers: one each in Michigan, Ohio, and California and two in New York. Management\n", - "is considering increasing the capacity of these data centers to keep up with increasing\n", - "demand. Each data center contains servers that are dedicated to Secure data and to \n", - "Super Secure data. The cost to update each data center and the resulting increase in\n", - "server capacity for each type of server are as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Data CenterCost ($ millions)Secure ServersSuper Secure Servers
Michigan2.55030
New York 118040
New York 23.54080
Ohio4.09060
California2.02030
\n", - "\n", - "The projected needs are for a total increase in capacity of 90 Secure servers and 90\n", - "Super Secure servers. Management wants to determine which data centers to update\n", - "to meet projected needs and, at the same time, minimize the total cost of the added\n", - "capacity.\n", - "Formulate a binary integer programming model that could be used to determine the\n", - "optimal solution to the capacity increase question facing management." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "C = 5 # Number of data centers\n", - "\n", - "P = [2.5, 3.5, 3.5, 4, 2] # Price to upgrade the data center\n", - "N = [50, 80, 40, 90, 20] # Nomral Server increase\n", - "S = [30, 40, 80, 60, 30] # Super Server increase\n", - "\n", - "R = 90 # Required increase in normal servers\n", - "T = 90 # Required increase in super servers\n", - "\n", - "m = Model('CloudServices')\n", - "\n", - "X = m.binary_var_list(C) \n", - "\n", - "summation = 0\n", - "for c in range(C):\n", - " summation += (X[c] * N[c])\n", - "m.add_constraint(summation >= R)\n", - "\n", - "summation = 0\n", - "for c in range(C):\n", - " summation += (X[c] * S[c])\n", - "m.add_constraint(summation >= T)\n", - "\n", - "summation = 0\n", - "for c in range(C):\n", - " summation += (X[c] * P[c])\n", - "\n", - "m.minimize(summation)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-09 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 15.500000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve added 1 rows and 1 columns.\n", - "MIP Presolve modified 9 coefficients.\n", - "Reduced MIP has 3 rows, 6 columns, and 13 nonzeros.\n", - "Reduced MIP has 5 binaries, 1 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "MIP Presolve eliminated 1 rows and 1 columns.\n", - "MIP Presolve added 1 rows and 1 columns.\n", - "Reduced MIP has 3 rows, 6 columns, and 13 nonzeros.\n", - "Reduced MIP has 5 binaries, 1 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.02 sec. (0.01 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 6 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.01 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 15.5000 0.0000 100.00%\n", - "* 0+ 0 6.0000 0.0000 100.00%\n", - " 0 0 5.5500 3 6.0000 5.5500 2 7.50%\n", - " 0 0 cutoff 6.0000 5.5500 2 7.50%\n", - "Elapsed time = 0.02 sec. (0.04 ticks, tree = 0.01 MB, solutions = 2)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.02 sec. (0.04 ticks)\n", - "Parallel b&c, 6 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.02 sec. (0.04 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=6,values={x4:1,x5:1})" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "objective: 6.000\n", - "status: OPTIMAL_SOLUTION(2)\n", - " x4=1\n", - " x5=1\n" - ] - } - ], - "source": [ - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/Component.lp b/Integer Linear Optimization Models/Component.lp deleted file mode 100644 index 2470ba3..0000000 --- a/Integer Linear Optimization Models/Component.lp +++ /dev/null @@ -1,137 +0,0 @@ -\ This file has been generated by DOcplex -\ ENCODING=ISO-8859-1 -\Problem name: ComponentOrdering - -Minimize - obj: 12 Amount_Bought_on_period_0 + 12 Amount_Bought_on_period_1 - + 12 Amount_Bought_on_period_2 + 12 Amount_Bought_on_period_3 - + 12 Amount_Bought_on_period_4 + 12 Amount_Bought_on_period_5 - + 12 Amount_Bought_on_period_6 + 12 Amount_Bought_on_period_7 - + 12 Amount_Bought_on_period_8 + 12 Amount_Bought_on_period_9 - + 12 Amount_Bought_on_period_10 + 12 Amount_Bought_on_period_11 - + 150 Whether_Bought_on_period_0 + 150 Whether_Bought_on_period_1 - + 150 Whether_Bought_on_period_2 + 150 Whether_Bought_on_period_3 - + 150 Whether_Bought_on_period_4 + 150 Whether_Bought_on_period_5 - + 150 Whether_Bought_on_period_6 + 150 Whether_Bought_on_period_7 - + 150 Whether_Bought_on_period_8 + 150 Whether_Bought_on_period_9 - + 150 Whether_Bought_on_period_10 + 150 Whether_Bought_on_period_11 - + Holding_0 + Holding_1 + Holding_2 + Holding_3 + Holding_4 + Holding_5 - + Holding_6 + Holding_7 + Holding_8 + Holding_9 + Holding_10 + Holding_11 -Subject To - c1: Whether_Bought_on_period_0 - _bool#1 = 0 - c2: Whether_Bought_on_period_1 - _bool#2 = 0 - c3: Whether_Bought_on_period_2 - _bool#3 = 0 - c4: Whether_Bought_on_period_3 - _bool#4 = 0 - c5: Whether_Bought_on_period_4 - _bool#5 = 0 - c6: Whether_Bought_on_period_5 - _bool#6 = 0 - c7: Whether_Bought_on_period_6 - _bool#7 = 0 - c8: Whether_Bought_on_period_7 - _bool#8 = 0 - c9: Whether_Bought_on_period_8 - _bool#9 = 0 - c10: Whether_Bought_on_period_9 - _bool#10 = 0 - c11: Whether_Bought_on_period_10 - _bool#11 = 0 - c12: Whether_Bought_on_period_11 - _bool#12 = 0 - c13: Holding_0 = 0 - c14: Holding_1 - Amount_Bought_on_period_0 = -20 - c15: Holding_2 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 = -40 - c16: Holding_3 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 = -70 - c17: Holding_4 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 = -110 - c18: Holding_5 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 = -250 - c19: Holding_6 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 - Amount_Bought_on_period_5 = -610 - c20: Holding_7 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 - Amount_Bought_on_period_5 - - Amount_Bought_on_period_6 = -1110 - c21: Holding_8 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 - Amount_Bought_on_period_5 - - Amount_Bought_on_period_6 - Amount_Bought_on_period_7 = -1650 - c22: Holding_9 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 - Amount_Bought_on_period_5 - - Amount_Bought_on_period_6 - Amount_Bought_on_period_7 - - Amount_Bought_on_period_8 = -2110 - c23: Holding_10 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 - Amount_Bought_on_period_5 - - Amount_Bought_on_period_6 - Amount_Bought_on_period_7 - - Amount_Bought_on_period_8 - Amount_Bought_on_period_9 = -2190 - c24: Holding_11 - Amount_Bought_on_period_0 - Amount_Bought_on_period_1 - - Amount_Bought_on_period_2 - Amount_Bought_on_period_3 - - Amount_Bought_on_period_4 - Amount_Bought_on_period_5 - - Amount_Bought_on_period_6 - Amount_Bought_on_period_7 - - Amount_Bought_on_period_8 - Amount_Bought_on_period_9 - - Amount_Bought_on_period_10 = -2190 - c25: Amount_Bought_on_period_0 <= 1000 - c26: Amount_Bought_on_period_1 <= 1000 - c27: Amount_Bought_on_period_2 <= 1000 - c28: Amount_Bought_on_period_3 <= 1000 - c29: Amount_Bought_on_period_4 <= 1000 - c30: Amount_Bought_on_period_5 <= 1000 - c31: Amount_Bought_on_period_6 <= 1000 - c32: Amount_Bought_on_period_7 <= 1000 - c33: Amount_Bought_on_period_8 <= 1000 - c34: Amount_Bought_on_period_9 <= 1000 - c35: Amount_Bought_on_period_10 <= 1000 - c36: Amount_Bought_on_period_11 <= 1000 - lc1: _bool#1 = 1 <-> Amount_Bought_on_period_0 >= 1 - lc2: _bool#2 = 1 <-> Amount_Bought_on_period_1 >= 1 - lc3: _bool#3 = 1 <-> Amount_Bought_on_period_2 >= 1 - lc4: _bool#4 = 1 <-> Amount_Bought_on_period_3 >= 1 - lc5: _bool#5 = 1 <-> Amount_Bought_on_period_4 >= 1 - lc6: _bool#6 = 1 <-> Amount_Bought_on_period_5 >= 1 - lc7: _bool#7 = 1 <-> Amount_Bought_on_period_6 >= 1 - lc8: _bool#8 = 1 <-> Amount_Bought_on_period_7 >= 1 - lc9: _bool#9 = 1 <-> Amount_Bought_on_period_8 >= 1 - lc10: _bool#10 = 1 <-> Amount_Bought_on_period_9 >= 1 - lc11: _bool#11 = 1 <-> Amount_Bought_on_period_10 >= 1 - lc12: _bool#12 = 1 <-> Amount_Bought_on_period_11 >= 1 - -Bounds - 0 <= Whether_Bought_on_period_0 <= 1 - 0 <= Whether_Bought_on_period_1 <= 1 - 0 <= Whether_Bought_on_period_2 <= 1 - 0 <= Whether_Bought_on_period_3 <= 1 - 0 <= Whether_Bought_on_period_4 <= 1 - 0 <= Whether_Bought_on_period_5 <= 1 - 0 <= Whether_Bought_on_period_6 <= 1 - 0 <= Whether_Bought_on_period_7 <= 1 - 0 <= Whether_Bought_on_period_8 <= 1 - 0 <= Whether_Bought_on_period_9 <= 1 - 0 <= Whether_Bought_on_period_10 <= 1 - 0 <= Whether_Bought_on_period_11 <= 1 - 0 <= _bool#1 <= 1 - 0 <= _bool#2 <= 1 - 0 <= _bool#3 <= 1 - 0 <= _bool#4 <= 1 - 0 <= _bool#5 <= 1 - 0 <= _bool#6 <= 1 - 0 <= _bool#7 <= 1 - 0 <= _bool#8 <= 1 - 0 <= _bool#9 <= 1 - 0 <= _bool#10 <= 1 - 0 <= _bool#11 <= 1 - 0 <= _bool#12 <= 1 - -Binaries - Whether_Bought_on_period_0 Whether_Bought_on_period_1 - Whether_Bought_on_period_2 Whether_Bought_on_period_3 - Whether_Bought_on_period_4 Whether_Bought_on_period_5 - Whether_Bought_on_period_6 Whether_Bought_on_period_7 - Whether_Bought_on_period_8 Whether_Bought_on_period_9 - Whether_Bought_on_period_10 Whether_Bought_on_period_11 _bool#1 _bool#2 _bool#3 - _bool#4 _bool#5 _bool#6 _bool#7 _bool#8 _bool#9 _bool#10 _bool#11 _bool#12 - -Generals - Amount_Bought_on_period_0 Amount_Bought_on_period_1 Amount_Bought_on_period_2 - Amount_Bought_on_period_3 Amount_Bought_on_period_4 Amount_Bought_on_period_5 - Amount_Bought_on_period_6 Amount_Bought_on_period_7 Amount_Bought_on_period_8 - Amount_Bought_on_period_9 Amount_Bought_on_period_10 Amount_Bought_on_period_11 - Holding_0 Holding_1 Holding_2 Holding_3 Holding_4 Holding_5 Holding_6 Holding_7 - Holding_8 Holding_9 Holding_10 Holding_11 -End diff --git a/Integer Linear Optimization Models/ComponentOrdering_Problem.ipynb b/Integer Linear Optimization Models/ComponentOrdering_Problem.ipynb deleted file mode 100644 index 346dd48..0000000 --- a/Integer Linear Optimization Models/ComponentOrdering_Problem.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Morgan Inc. is planning the purchase of one of the component\n", - "parts it needs for its finished product. The anticipated demands for the component for\n", - "the next 12 periods are shown in the following table.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Period123456789101112
Demand2020304014036050054046080020
\n", - "\n", - " The cost to order the component\n", - "(labor, shipping, and paperwork) is $150. The cost to hold these components in inventory is $1 per component per period. The price of the component is expected to remain\n", - "stable at $12 per unit for the next 12 periods, and no quantity discounts are available.\n", - "The maximum order size is 1,000 units.\n", - "Formulate a model to minimize the total cost of satisfying Morgan Inc.’s demand\n", - "for this component." - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "P = 12\n", - "D = [20, 20, 30, 40, 140, 360, 500, 540, 460, 80, 0, 20]\n", - "\n", - "init_amount = 150\n", - "h = 1\n", - "c = 12\n", - "\n", - "m = Model('ComponentOrdering')\n", - "\n", - "X = m.integer_var_list(12, name = 'Amount Bought on period') # How much to purchase\n", - "XX = m.binary_var_list(12, name = 'Whether Bought on period')\n", - "Y = m.integer_var_list(12, name = 'Holding') # Holding\n", - "\n", - "for i in range(P):\n", - " m.add_constraint(XX[i] == (X[i] >= 1))\n", - "\n", - "for i in range(P):\n", - " old_x_summation = 0\n", - " old_d_summation = 0\n", - " for u in range(i):\n", - " old_x_summation += X[u]\n", - " old_d_summation += D[u]\n", - " m.add_constraint(Y[i] == old_x_summation - old_d_summation)\n", - "\n", - "for i in range(P):\n", - " m.add_constraint(X[i] <= 1000)\n", - "\n", - "summation = 0\n", - "for i in range(P):\n", - " summation += X[i] * c\n", - " summation += Y[i] * h\n", - " summation += XX[i] * init_amount\n", - "\n", - "m.minimize(summation)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Component.lp'" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.export_as_lp(\"Component.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-09 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 2 times.\n", - "MIP Presolve eliminated 14 rows and 3 columns.\n", - "Aggregator did 12 substitutions.\n", - "Reduced MIP has 32 rows, 33 columns, and 119 nonzeros.\n", - "Reduced MIP has 11 binaries, 22 generals, 0 SOSs, and 11 indicators.\n", - "Presolve time = 0.00 sec. (0.07 ticks)\n", - "Found incumbent of value 189300.000000 after 0.00 sec. (0.11 ticks)\n", - "Probing time = 0.00 sec. (0.01 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "MIP Presolve eliminated 2 rows and 2 columns.\n", - "Reduced MIP has 30 rows, 31 columns, and 115 nonzeros.\n", - "Reduced MIP has 10 binaries, 21 generals, 0 SOSs, and 10 indicators.\n", - "Presolve time = 0.00 sec. (0.07 ticks)\n", - "Probing time = 0.00 sec. (0.01 ticks)\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 6 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.06 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 189300.0000 390.0000 99.79%\n", - "* 0+ 0 187278.0000 390.0000 99.79%\n", - " 0 0 26755.5000 9 187278.0000 26755.5000 9 85.71%\n", - "* 0+ 0 27780.0000 26755.5000 3.69%\n", - " 0 0 27422.6154 10 27780.0000 Cuts: 24 22 1.29%\n", - "* 0+ 0 27776.0000 27422.6154 1.27%\n", - "* 0+ 0 27754.0000 27422.6154 1.19%\n", - " 0 0 27427.4359 10 27754.0000 Cuts: 5 24 1.18%\n", - "* 0+ 0 27751.0000 27427.4359 1.17%\n", - "* 0+ 0 27707.0000 27427.4359 1.01%\n", - "* 0+ 0 27460.0000 27427.4359 0.12%\n", - " 0 0 cutoff 27460.0000 27460.0000 24 0.00%\n", - "Elapsed time = 0.11 sec. (2.50 ticks, tree = 0.01 MB, solutions = 8)\n", - "\n", - "Mixed integer rounding cuts applied: 3\n", - "Lift and project cuts applied: 1\n", - "Gomory fractional cuts applied: 8\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.11 sec. (2.50 ticks)\n", - "Parallel b&c, 6 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.11 sec. (2.50 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=27460,values={Amount Bought on per.." - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "objective: 27460\n", - "status: OPTIMAL_SOLUTION(2)\n", - " \"Amount Bought on period_0\"=110\n", - " \"Amount Bought on period_4\"=140\n", - " \"Amount Bought on period_5\"=360\n", - " \"Amount Bought on period_6\"=500\n", - " \"Amount Bought on period_7\"=540\n", - " \"Amount Bought on period_8\"=540\n", - " \"Whether Bought on period_0\"=1\n", - " \"Whether Bought on period_4\"=1\n", - " \"Whether Bought on period_5\"=1\n", - " \"Whether Bought on period_6\"=1\n", - " \"Whether Bought on period_7\"=1\n", - " \"Whether Bought on period_8\"=1\n", - " \"Holding_1\"=90\n", - " \"Holding_2\"=70\n", - " \"Holding_3\"=40\n", - " \"Holding_4\"=0\n", - " \"Holding_9\"=80\n" - ] - } - ], - "source": [ - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/EastborneRealty_Problem.ipynb b/Integer Linear Optimization Models/EastborneRealty_Problem.ipynb deleted file mode 100644 index 4b2dc9a..0000000 --- a/Integer Linear Optimization Models/EastborneRealty_Problem.ipynb +++ /dev/null @@ -1,106 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eastborne Realty has $2 million available for the purchase of new rental property. After an\n", - "initial screening, Eastborne reduced the investment alternatives to townhouses and apartment buildings. Each townhouse can be purchased for $282,000, and five are available.\n", - "Each apartment building can be purchased for $400,000, and the developer will construct\n", - "as many buildings as Eastborne wants to purchase.\n", - "Eastborne’s property manager can devote up to 140 hours per month to these new properties; each townhouse is expected to require 4 hours per month, and each apartment building\n", - "is expected to require 40 hours per month. The annual cash flow, after deducting mortgage\n", - "payments and operating expenses, is estimated to be $10,000 per townhouse and $15,000\n", - "per apartment building. Eastborne’s owner would like to determine the number of townhouses and the number of apartment buildings to purchase to maximize annual cash flow." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets and Indicies\n", - "\n", - "$\\mathcal{T}$: Types of investments
\n", - "$t, t \\in \\mathcal{T}$: An index in set $\\mathcal{T}$ \n", - "\n", - "#### Data\n", - "\n", - "$q$: Money Budget given
\n", - "$w$: Time Budget given
\n", - "\n", - "$H$: Column vector representing hours needed per month for each investment type
\n", - "$P$: Column vector representing Purchase price for each investment type
\n", - "$F$: Column vector representing Annual cash flow for each investment type
\n", - "\n", - "$h_t \\in H, t \\in \\mathcal{T}$: Hours needed per month for each investment type
\n", - "$p_t \\in P, t \\in \\mathcal{T}$: Purchase price for each investment type
\n", - "$f_t \\in F, t \\in \\mathcal{T}$: Annual cash flow for each investment type
\n", - "\n", - "#### Decision Variables\n", - "\n", - "$X$: Column vector representing Number of types of investments to purchase
\n", - "\n", - "$x_t, t \\in \\mathcal{T}$: Number of types of investments to purchase\n", - "\n", - "#### Function\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{maximize }} & \\; F^T X \\\\\n", - "\\text{subject to:} \\\\\n", - "& P^T X \\leq q\\\\ \n", - "& H^T X \\leq w\\\\ \n", - "& t_1 \\leq 5\\\\\n", - "& x_i \\geq 0 \\; \\text{and integer} \\;, \\forall i\\in\\mathcal{T}\n", - "\\end{align*}\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sets\n", - "Types_of_investment = {'townhouses', 'aparments'}\n", - "\n", - "\n", - "budget_money = 2000000\n", - "budget_time = 140\n", - "price_cost = [282000, 400000]\n", - "time_cost = [4, 40]\n", - "limit_townhouses = 5\n", - "\n", - "revenue = [10000, 15000]\n", - "\n", - "max_revenue = " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "maximize " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/HartManufacturing_Problem.ipynb b/Integer Linear Optimization Models/HartManufacturing_Problem.ipynb deleted file mode 100644 index 8ef440b..0000000 --- a/Integer Linear Optimization Models/HartManufacturing_Problem.ipynb +++ /dev/null @@ -1,119 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NOT SURE ABOUT THIS ONE'S SOLUTION" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hart Manufacturing makes three products.\n", - "Each product requires manufacturing operations in three departments: A, B, and C. The\n", - "labor-hour requirements, by department, are as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DepartmentProduct 1Product 2Product 3
A1.503.002.00
B2.001.002.50
C0.250.250.25
\n", - "\n", - "During the next production period the labor-hours available are 450 in department A,\n", - "350 in department B, and 50 in department C. The profit contributions per unit are $25\n", - "for product 1, $28 for product 2, and $30 for product 3.\n", - "Formulate a linear programming model for maximizing total profit contribution." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets and Indicies\n", - "\n", - "$\\mathcal{D}$: The Departments
\n", - "$\\mathcal{P}$: The Products
\n", - "\n", - "$d \\in \\mathcal{D}$: An index in set $\\mathcal{D}$
\n", - "$p \\in \\mathcal{P}$: An index in set $\\mathcal{P}$
\n", - "\n", - "#### Data\n", - "\n", - "$R$: Matrix representing the number of hours needed for each Product in $\\mathcal{P}$ for each department in $\\mathcal{D}$.
\n", - "$r_{dp} \\in R, d \\in \\mathcal{D}, p \\in \\mathcal{P}$: An index in set $R$.
\n", - "\n", - "$H$: Column vector representing the number of hours available for each department in $\\mathcal{D}$.
\n", - "$h_d \\in H, d \\in \\mathcal{D}$: An index in set $H$.
\n", - "\n", - "$F$: Column vector representing the profit per Product in $\\mathcal{P}$
\n", - "$f_p \\in F, p \\in \\mathcal{P}$: An index in set $P$.
\n", - "\n", - "#### Decision Variables\n", - "\n", - "$X$: Matrix representing the number of products for each department
\n", - "$x_{dp} \\in X, d \\in \\mathcal{D}, p \\in \\mathcal{P}$: An index in the $X$
\n", - "\n", - "#### Function\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{maximize }} & \\; (1_{||D||} X F^T) - (1_{||D||} X R^T 1_{||D||}^T )\\\\\n", - "\\text{subject to:} \\\\\n", - "& X R^T 1_{||D||}^T \\leq H\\\\ \n", - "& x_{iu} \\geq 0\\ \\text{and Integer}; \\forall i\\in\\mathcal{D}, \\forall u\\in\\mathcal{P}\n", - "\\end{align*}\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/InvestmentNet_Problem.ipynb b/Integer Linear Optimization Models/InvestmentNet_Problem.ipynb deleted file mode 100644 index 7fee4a4..0000000 --- a/Integer Linear Optimization Models/InvestmentNet_Problem.ipynb +++ /dev/null @@ -1,269 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Spencer Enterprises is attempting to choose among\n", - "a series of new investment alternatives. The potential investment alternatives, the net\n", - "present value of the future stream of returns, the capital requirements, and the available\n", - "capital funds over the next three years are summarized as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
AlternativeNet Present Value ($)Year 1Year 2Year 3
Limited warehouse expansion4,0003,0001,0004,000
Extensive warehouse expansion6,0002,5003,5003,500
Test market new product10,5006,0004,0005,000
Advertising campaign4,0002,0001,5001,800
Basic research8,0005,0001,0004,000
Purchase new equipment3,0001,000500900
Capital funds available-10,5007,0008,750
\n", - "\n", - "Develop and solve an integer programming model for maximizing the net present value." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets and indicies

\n", - "\n", - "$\\mathcal{I}$: Investments, where each investment is denoted as $i$. |$\\mathcal{I}$| = $\\textbf{I}$
\n", - "$\\mathcal{Y}$: Years, where each year is denoted as $y$. |$\\mathcal{Y}$| = $\\textbf{Y}$
\n", - "\n", - "

Data

\n", - "\n", - "$N$: Column vector representing Net present values for each Investment in $\\mathcal{I}$. Where each element is represented as $n_i$.
\n", - "$R$: Matrix representing Capital Funds for each Investment in $\\mathcal{I}$ for each year in $\\mathcal{Y}$. Where each element is denoted as $r_{iy}$.
\n", - "$A$: Column vector representing Available Capital Funds for each year in $\\mathcal{Y}$, where each item is denoted as $a_y$.
\n", - "\n", - "

Decision Variables

\n", - "\n", - "$X$: Which investments in $\\mathcal{I}$ to open, where each item is denoted as $x_i$.
\n", - "\n", - "

Function

\n", - "\n", - "\\begin{align*}\n", - "\n", - "\\mathrm{maximize} \\sum_{u=1}^{\\textbf{I}} x_in_i \\\\\n", - "\\text{subject to} \\\\\n", - "& \\sum_{u=0}^{\\textbf{I}}{x_ir_{iy}} \\leq a_y ; \\; \\forall y \\in \\mathcal{Y} \\\\\n", - "& x_i \\in \\{1, 0\\} ; \\; \\forall i \\in \\mathcal{I}\n", - "\\end{align*}\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "I = 6\n", - "Y = 3\n", - "\n", - "N = [4000, 6000, 10500, 4000, 8000, 3000]\n", - "\n", - "R = [\n", - " [3000, 1000, 4000],\n", - " [2500, 3500, 3500],\n", - " [6000, 4000, 5000],\n", - " [2000, 1500, 1800],\n", - " [5000, 1000, 4000],\n", - " [1000, 500 ,900]\n", - "]\n", - "\n", - "A = [10500, 7000, 8750]\n", - "\n", - "m = Model(name = 'Investment Net')\n", - "\n", - "X = m.binary_var_list(len(N))\n", - "\n", - "for y in range(Y):\n", - " summation = 0\n", - " for u in range(I):\n", - " summation += (X[u] * R[u][y])\n", - " m.add_constraint(summation <= A[y])\n", - "\n", - "function_summation = 0\n", - "for u in range(I):\n", - " function_summation += (X[u] * N[u])\n", - "\n", - "m.maximize(function_summation)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-09 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 0.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve modified 13 coefficients.\n", - "Reduced MIP has 3 rows, 6 columns, and 18 nonzeros.\n", - "Reduced MIP has 6 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "Reduced MIP has 3 rows, 6 columns, and 18 nonzeros.\n", - "Reduced MIP has 6 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Clique table members: 3.\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 6 threads.\n", - "Root relaxation solution time = 0.03 sec. (0.01 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 0.0000 35500.0000 --- \n", - "* 0+ 0 17000.0000 35500.0000 108.82%\n", - " 0 0 18480.0000 2 17000.0000 18480.0000 2 8.71%\n", - "* 0 0 integral 0 17500.0000 17500.0000 4 0.00%\n", - "Elapsed time = 0.11 sec. (0.08 ticks, tree = 0.01 MB, solutions = 3)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.11 sec. (0.08 ticks)\n", - "Parallel b&c, 6 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.11 sec. (0.08 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=17500,values={x3:1,x4:1,x6:1})" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "status = integer optimal solution\n", - "time = 0.125 s.\n", - "problem = MILP\n", - "gap = 0%\n", - "\n", - "objective: 17500\n", - "status: OPTIMAL_SOLUTION(2)\n", - " x3=1\n", - " x4=1\n", - " x6=1\n" - ] - } - ], - "source": [ - "print(m.solve_details)\n", - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/KingInc_problem.ipynb b/Integer Linear Optimization Models/KingInc_problem.ipynb deleted file mode 100644 index e3caa1e..0000000 --- a/Integer Linear Optimization Models/KingInc_problem.ipynb +++ /dev/null @@ -1,80 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "King City Inc. manufactures machine tools. The\n", - "production planner who oversees the production of two of King City’s machines needs\n", - "to determine how many of each to produce this month. The two machines, TopLathe\n", - "and BigPress, each require a certain common component. Each TopLathe requires 10\n", - "of these components and each BigPress requires 7. Only 49 components are available\n", - "this month. The sales department requires that the total number of machines produced\n", - "in a month must be at least 5 (the number TopLathes plus the number BigPresses must\n", - "be at least 5). The profit for a TopLathe is $50,000 and $34,000 for a BigPress." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets and Indicies\n", - "\n", - "$\\mathcal{M}$: Machine types
\n", - "$m, m \\in \\mathcal{M}$: An index in set $\\mathcal{M}$
\n", - "\n", - "#### Data \n", - "\n", - "$a$: Available components
\n", - "$m$: Minimum Required number of all machines to be created
\n", - "$R$: A column vector representing the required common components for the Machine Types.
\n", - "$r_m \\in R, m \\in \\mathcal{M}$: An index representing the required common components for each Machine Type.
\n", - "$P$: A column vector representing the profit for the Machine Types.
\n", - "$p_m \\in P, m \\in \\mathcal{M}$: An index representing the profit for each Machine Type.
\n", - "\n", - "#### Decision Variables\n", - "\n", - "$X$: A column vector representing the amount of machine to create.
\n", - "$x_m \\in X, m \\in \\mathcal{M}$: An index representing the amount to create for each machine types
\n", - "\n", - "#### Function\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{maximize }} & \\; P^T X \\\\\n", - "\\text{subject to:} \\\\\n", - "& 1_{||X||}^T X \\leq m\\\\ \n", - "& R^T X \\leq a\\\\ \n", - "& x_i \\geq 0 \\; \\text{and integer} \\;, \\forall i\\in\\mathcal{M}\n", - "\\end{align*}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/MartinBeckCompany_Problem.ipynb b/Integer Linear Optimization Models/MartinBeckCompany_Problem.ipynb deleted file mode 100644 index 1463c51..0000000 --- a/Integer Linear Optimization Models/MartinBeckCompany_Problem.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Martin-Beck Company operates a plant in St.\n", - "Louis with an annual capacity of 30,000 units. Product is shipped to regional distribution centers located in Boston, Atlanta, and Houston. Because of an anticipated\n", - "increase in demand, Martin-Beck plans to increase capacity by constructing a new\n", - "plant in one or more of the following cities: Detroit, Toledo, Denver, or Kansas City.\n", - "The estimated annual fixed cost and the annual capacity for the four proposed plants\n", - "are as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Proposed PlantAnnual Fixed CostAnnual Capacity
Detroit$175,00010,000
Toledo$300,00020,000
Denver$375,00030,000
Kansas City$500,00040,000
\n", - "\n", - "The company’s long-range planning group developed forecasts of the anticipated\n", - "annual demand at the distribution centers as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Distribution CenterAnnual Demand
Boston30,000
Atlanta20,000
Houston20,000
\n", - "\n", - "The shipping cost per unit from each plant to each distribution center is as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Plant SiteBostonAtlantaHouston
Detroit523
Toledo434
Denver975
Kansas City1042
St. Louis843
\n", - "\n", - "Formulate a mixed-integer programming model that could be used to help\n", - "Martin-Beck determine which new plant or plants to open in order to satisfy\n", - "anticipated demand." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets and Indicies\n", - "\n", - "$\\mathcal{P}$: Plants
\n", - "$\\mathcal{D}$: Distrubution Centers
\n", - "\n", - "$p \\in \\mathcal{P}$: An index in the set $\\mathcal{P}$, where $p_2, p_3, p_4, p_5$ are proposed plants
\n", - "$d \\in \\mathcal{D}$: An index in the set $\\mathcal{D}$
\n", - "\n", - "#### Data\n", - "\n", - "$F$: A column vector representing Fixed cost for the Plants
\n", - "$f_p \\in F, p \\in \\mathcal{P}$: An index representing Fixed cost for each the Plants
\n", - "$f_1$: Equals 0, since it is already built.\n", - "\n", - "$C$: A column vector representing Capacity for the Plants
\n", - "$c_p \\in C, p \\in \\mathcal{P}$: An index representing Capacity for each the Plants
\n", - "\n", - "$M$: A column vector representing Demand for the Distribution Centers
\n", - "$m_d \\in M, d \\in \\mathcal{D}$: An index representing Demand for the Distribution Centers
\n", - "\n", - "$S$: A matrix represnting the Shipping Cost from each plant to each distribution center
\n", - "$s_{pd} \\in S, p \\in \\mathcal{P}, d \\in \\mathcal{D}$: an Index in the set $S$
\n", - " \n", - "#### Decision Variables\n", - "\n", - "$X$: A column vector representing which plants to build
\n", - "$x_p \\in X, p \\in \\mathcal{P}$: An index in the set $X$
\n", - "\n", - "$Y$: A matrix representing the amount to ship between each distrubution center and plant
\n", - "$y_pd \\in Y, p \\in \\mathcal{P}, d \\in \\mathcal{D}$: An index in the set $Y$
\n", - "\n", - "\n", - "#### Function\n", - "\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{minimize }} & \\; (F^T X) + 1_{||D||}^TYS1_{||D||}\\\\\n", - "\\text{subject to:} \\\\\n", - "& Y1_{||D||} \\leq C\\\\ \n", - "& 1_{||P||}Y \\leq M^T\\\\ \n", - "& x_i \\in \\{0, 1\\} ; \\forall i\\in\\mathcal{P}\\\\\n", - "& y_{iu} \\geq 0\\ \\text{and Integer}; \\forall i\\in\\mathcal{P}, \\forall u\\in\\mathcal{D}\n", - "\\end{align*}" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/MutualFundPortfolio_Problem.ipynb b/Integer Linear Optimization Models/MutualFundPortfolio_Problem.ipynb deleted file mode 100644 index 31b00d7..0000000 --- a/Integer Linear Optimization Models/MutualFundPortfolio_Problem.ipynb +++ /dev/null @@ -1,196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dave has $100,000 to invest in 10 mutual fund\n", - "alternatives with the following restrictions. For diversification, no more than $25,000\n", - "can be invested in any one fund. If a fund is chosen for investment, then at least\n", - "$10,000 will be invested in it. No more than two of the funds can be pure growth\n", - "funds, and at least one pure bond fund must be selected. The total amount invested in\n", - "pure bond funds must be at least as much as the amount invested in pure growth funds.\n", - "Using the following expected returns, formulate and solve a model that will determine\n", - "the investment strategy that will maximize expected annual return. What assumptions\n", - "have you made in your model? How often would you expect to run your model?" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "investment =[1,2,3,4,5,6,7,8,9,10]\n", - "return_type = [\"Growth\",\"Growth\",\"Growth\",\"Growth\",\"Growth & Income\",\"Growth & Income\",\"Growth & Income\",\"Stock & Bond\",\"Bond\",\"Bond\"]\n", - "return_pert = [0.0670,0.0765,0.0755,0.0745,0.0750,0.0645,0.0705,0.0690,0.0520,0.0590]\n", - "return_type_int = [0, 0, 0, 0, 1, 1, 1, 2, 3, 3]\n", - "\n", - "import numpy as np\n", - "def create_dummies(int_list):\n", - " dummies = np.zeros((len(int_list), len(np.unique(int_list))))\n", - "\n", - " for i in range(len(int_list)):\n", - " dummies[i, int_list[i]] = 1\n", - "\n", - " return dummies\n", - "\n", - "dummies = create_dummies(return_type_int)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "X = m.integer_var_list(investment, lb = 0, ub = 25000, name = 'Amount Investment')\n", - "XX = m.binary_var_list(investment, name = 'Chosen Investment')\n", - "\n", - "for i in range(len(X)):\n", - " m.add_indicator(XX[i], X[i] >= 1, 1)\n", - " m.add_indicator(XX[i], X[i] <= 0, 0)\n", - "\n", - "for i in range(len(X)):\n", - " m.add_constraint(10000 * XX[i] <= X[i])\n", - "\n", - "m.add_constraint(sum([X[i] for i in range(len(X))]) <= 100000)\n", - "m.add_constraint(sum([XX[i] * dummies[i][0] for i in range(len(X))]) <= 2)\n", - "m.add_constraint(sum([XX[i] * dummies[i][3] for i in range(len(X))]) >= 1)\n", - "\n", - "m.maximize(sum([X[i] * return_pert[i] for i in range(len(X))]))" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'MutualFunds.lp'" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.export_as_lp('MutualFunds.lp')" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 1 time.\n", - "Reduced MIP has 23 rows, 20 columns, and 56 nonzeros.\n", - "Reduced MIP has 10 binaries, 10 generals, 0 SOSs, and 10 indicators.\n", - "Presolve time = 0.00 sec. (0.03 ticks)\n", - "Found incumbent of value 590.000000 after 0.01 sec. (0.08 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "MIP Presolve eliminated 10 rows and 0 columns.\n", - "Reduced MIP has 13 rows, 20 columns, and 36 nonzeros.\n", - "Reduced MIP has 10 binaries, 10 generals, 0 SOSs, and 10 indicators.\n", - "Presolve time = 0.01 sec. (0.04 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Clique table members: 1.\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 4 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.02 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 590.0000 17087.5000 --- \n", - " 0 0 7382.5000 4 590.0000 7382.5000 4 --- \n", - " 0 0 7322.5000 1 590.0000 Impl Bds: 4 9 --- \n", - "* 0+ 0 7322.5000 7322.5000 0.00%\n", - " 0 0 cutoff 7322.5000 7322.5000 9 0.00%\n", - "Elapsed time = 0.13 sec. (0.24 ticks, tree = 0.01 MB, solutions = 2)\n", - "\n", - "Implied bound cuts applied: 4\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.14 sec. (0.24 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.14 sec. (0.24 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model19\n", - "objective: 7322.5\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Amount Investment_2=25000\n", - "Amount Investment_3=25000\n", - "Amount Investment_5=25000\n", - "Amount Investment_7=15000\n", - "Amount Investment_10=10000\n", - "Chosen Investment_2=1\n", - "Chosen Investment_3=1\n", - "Chosen Investment_5=1\n", - "Chosen Investment_7=1\n", - "Chosen Investment_10=1\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/Notes.md b/Integer Linear Optimization Models/Notes.md deleted file mode 100644 index 7c5bd45..0000000 --- a/Integer Linear Optimization Models/Notes.md +++ /dev/null @@ -1,12 +0,0 @@ -Types of integer linear program: -- All integer linear program: - - All decision variables are integers. -- LP relaxation: - - Problems where you can turn an ILP into an LP, by dropping integer constraint. - - If the optimal decision variables solution for LP relaxation are Integer, then it is the optimal solution for the original ILP problem. -- Mixed Integer linear program: - - Some decision variables are integers. -- Binary integer linear program: - - The decision variables can only be either 0 or 1. - -ALWAYS USE <= OR >=. DO NOT USE < OR > ONLY diff --git a/Integer Linear Optimization Models/PoliceSubstation.lp b/Integer Linear Optimization Models/PoliceSubstation.lp deleted file mode 100644 index e0cdad7..0000000 --- a/Integer Linear Optimization Models/PoliceSubstation.lp +++ /dev/null @@ -1,20 +0,0 @@ -\ This file has been generated by DOcplex -\ ENCODING=ISO-8859-1 -\Problem name: Assignment - -Minimize - obj: x_0 + x_1 + x_2 + x_3 + x_4 + x_5 + x_6 -Subject To - c1: x_0 + x_1 + x_2 + x_6 >= 1 - c2: x_1 + x_3 >= 1 - c3: x_2 + x_4 >= 1 - c4: x_3 + x_4 + x_5 >= 1 - c5: x_0 + x_1 + x_2 + x_3 + x_5 + x_6 >= 1 - c6: x_4 + x_5 + x_6 >= 1 - c7: x_0 + x_1 + x_4 + x_6 >= 1 - -Bounds - -Generals - x_0 x_1 x_2 x_3 x_4 x_5 x_6 -End diff --git a/Integer Linear Optimization Models/PoliceSubstations_Problem.ipynb b/Integer Linear Optimization Models/PoliceSubstations_Problem.ipynb deleted file mode 100644 index 63291e6..0000000 --- a/Integer Linear Optimization Models/PoliceSubstations_Problem.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Locating Police Substations. Grave City is considering the relocation of several\n", - "police substations to obtain better enforcement in high-crime areas. The locations\n", - "under consideration together with the areas that can be covered from these locations\n", - "are given in the following table:\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Potential Locations
\n", - " for Substations		Areas Covered
A1, 5, 7
B1, 2, 5, 7
C1, 3, 5
D2, 4, 5
E3, 4, 6
F4, 5, 6
G1, 5, 6, 7
\n", - "\n", - "Formulate an integer programming model that could be used to find the minimum\n", - "number of locations necessary to provide coverage to all areas." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets and Indicies

\n", - "\n", - "$\\mathcal{L}$: Locations to open a station in, where each location is represnted as $l$. |$\\mathcal{L}$| = $\\textbf{L}$
\n", - "$\\mathcal{A}$: Areas to cover, where each area is represnted as $a$. |$\\mathcal{A}$| = $\\textbf{A}$
\n", - "\n", - "

Data

\n", - "\n", - "$C$: a matrix representing which areas in $\\mathcal{A}$ can be covered by each location in $\\mathcal{L}$, where each element is represented as $c_{la}$.\n", - "\n", - "

Decision Variables

\n", - "\n", - "$X$: A column vector respresnting which locations to open a station in.
\n", - "\n", - "

Function

\n", - "\n", - "\\begin{align*}\n", - "\\ \\mathrm{minimize} \\sum_{i=1}^{\\textbf{L}}{x_{i}} \\\\\n", - "\\ \\text{subject to} \\\\\n", - "& \\sum_{i=1}^{\\textbf{L}} c_{ia} * x_{i} \\geq 1 ; \\; \\forall a \\in \\mathcal{A} \\\\\n", - "& x_{l} \\in \\{1, 0\\} ; \\; \\forall l \\in \\mathcal{L}\n", - "\\end{align*}" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'PoliceSubstation.lp'" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from docplex.mp.model import Model\n", - "import numpy as np\n", - "import json\n", - "\n", - "### Numbers too large to solve.\n", - "L = 7\n", - "A = 7\n", - "\n", - "C = [\n", - "\t[1, 0, 0, 0, 1, 0, 1],\n", - "\t[1, 1, 0, 0, 1, 0, 1],\n", - "\t[1, 0, 1, 0, 1, 0, 0],\n", - "\t[0, 1, 0, 1, 1, 0, 0],\n", - "\t[0, 0, 1, 1, 0, 1, 1],\n", - "\t[0, 0, 0, 1, 1, 1, 0],\n", - "\t[1, 0, 0, 0, 1, 1, 1]\n", - "]\n", - "\n", - "m = Model(name = 'Assignment')\n", - "\n", - "dv = m.integer_var_list(L, name = 'x')\n", - "\n", - "for a in range(A):\n", - " summation = 0\n", - " for i in range(L):\n", - " summation += C[i][a] * dv[i] \n", - "\n", - " m.add_constraint(summation >= 1)\n", - "\n", - "function_summation = 0\n", - "\n", - "for i in range(L):\n", - " function_summation += dv[i] \n", - "\n", - "m.minimize(function_summation)\n", - "\n", - "m.export_as_lp(\"PoliceSubstation.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-09 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 5.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 2 rows and 2 columns.\n", - "Reduced MIP has 5 rows, 5 columns, and 11 nonzeros.\n", - "Reduced MIP has 5 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "Reduced MIP has 5 rows, 5 columns, and 11 nonzeros.\n", - "Reduced MIP has 5 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Clique table members: 4.\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 6 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.01 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 4.0000 0.0000 100.00%\n", - "* 0+ 0 3.0000 0.0000 100.00%\n", - "* 0 0 integral 0 2.0000 2.0000 4 0.00%\n", - "Elapsed time = 0.02 sec. (0.04 ticks, tree = 0.00 MB, solutions = 3)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.02 sec. (0.04 ticks)\n", - "Parallel b&c, 6 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.02 sec. (0.04 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=2,values={x_1:1,x_4:1})" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JobSolveStatus.OPTIMAL_SOLUTION\n", - "objective: 2\n", - "status: OPTIMAL_SOLUTION(2)\n", - " x_1=1\n", - " x_4=1\n" - ] - } - ], - "source": [ - "print(m.solve_status)\n", - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/ProductDesign_Problem.ipynb b/Integer Linear Optimization Models/ProductDesign_Problem.ipynb deleted file mode 100644 index 82a2ddb..0000000 --- a/Integer Linear Optimization Models/ProductDesign_Problem.ipynb +++ /dev/null @@ -1,218 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Burnside Marketing Research conducted a study for Barker Foods\n", - "on several formulations for a new dry cereal. Three attributes were found to be most\n", - "influential in determining which cereal had the best taste: ratio of wheat to corn\n", - "in the cereal flake, type of sweetener (sugar, honey, or artificial), and the presence\n", - "or absence of flavor bits. Seven children participated in taste tests and provided\n", - "the following part-worths for the attributes (see Section 13.4 for a discussion of\n", - "part-worths):\n", - "\n", - "a. Suppose that the overall utility (sum of part-worths) of the current favorite cereal\n", - "is 75 for each child. What product design will maximize the number of children in\n", - "the sample who prefer the new dry cereal? Note that a child will prefer the new dry\n", - "cereal only if its overall utility is at least 1 part-worth larger than the utility of their\n", - "current preferred cereal.\n", - "\n", - "b. Assume that the overall utility of the current favorite cereal for children 1 to 4\n", - "is 70, and the overall utility of the current favorite cereal for children 5 to 7 is 80.\n", - "What product design will maximize the number of children in the sample who\n", - "prefer the new dry cereal? Note that a child will prefer the new dry cereal only if\n", - "its overall utility is at least 1 part-worth larger than the utility of their current pre-\n", - "ferred cereal." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Maybe correct?" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Child | Wheat/Corn | Sweetener | Flavor Bits\n", - "# | Low High | Sugar Honey Artificial| Present Absent\n", - "# 1 | 15 35 | 30 40 25 | 15 9\n", - "# 2 | 30 20 | 40 35 35 | 8 11\n", - "# 3 | 40 25 | 20 40 10 | 7 14\n", - "# 4 | 35 30 | 25 20 30 | 15 18\n", - "# 5 | 25 40 | 40 20 35 | 18 14\n", - "# 6 | 20 25 | 20 35 30 | 9 16\n", - "# 7 | 30 15 | 25 40 40 | 20 11\n", - "\n", - "Child = [1, 2, 3, 4, 5, 6, 7]\n", - "Wheat_opts = ['Low', 'High']\n", - "Sweetener_opts = ['Sugar', 'Honey', 'Artificial']\n", - "Flavor_opts = ['Present', 'Absent']\n", - "\n", - "Wheat = [[15,30,40,35,25,20,30],\n", - " [35,20,25,30,40,25,15]]\n", - "\n", - "Sweetener = [[30,40,20,25,40,20,25],\n", - " [40,35,40,20,20,35,40],\n", - " [25,35,10,30,35,30,40]]\n", - "\n", - "Flavor = [[15,8,7,15,18,9,20],\n", - " [9, 11,14,18,14,16,11]]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "X_Wheat = m.binary_var_list(Wheat_opts)\n", - "X_Sweetener = m.binary_var_list(Sweetener_opts)\n", - "X_Flavor = m.binary_var_list(Flavor_opts)\n", - "\n", - "# Y_children = m.integer_var_list(len(Child), name = 'Child')\n", - "YY_children = m.binary_var_list(Child, name = 'Child')\n", - "\n", - "m.add_constraint(sum(X_Wheat) == 1)\n", - "m.add_constraint(sum(X_Sweetener) == 1)\n", - "m.add_constraint(sum(X_Flavor) == 1)\n", - "\n", - "for child in range(len(YY_children)):\n", - " summation = 0\n", - " for wheet_opt in range(len(Wheat_opts)):\n", - " summation += X_Wheat[wheet_opt] * Wheat[wheet_opt][child]\n", - "\n", - " for sweetener_opt in range(len(Sweetener_opts)):\n", - " summation += X_Sweetener[sweetener_opt] * Sweetener[sweetener_opt][child]\n", - "\n", - " for flavor_opt in range(len(Flavor_opts)):\n", - " summation += X_Flavor[flavor_opt] * Flavor[flavor_opt][child]\n", - "\n", - " m.add_indicator(YY_children[child], summation >= 76)\n", - "\n", - "m.export_as_lp('ProductDesign.lp')\n", - "m.maximize(sum(YY_children))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 0.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 2 times.\n", - "MIP Presolve modified 3 coefficients.\n", - "Aggregator did 2 substitutions.\n", - "Reduced MIP has 15 rows, 19 columns, and 58 nonzeros.\n", - "Reduced MIP has 12 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.01 sec. (0.08 ticks)\n", - "Probing time = 0.00 sec. (0.02 ticks)\n", - "Cover probing fixed 0 vars, tightened 6 bounds.\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "MIP Presolve modified 12 coefficients.\n", - "Reduced MIP has 15 rows, 19 columns, and 58 nonzeros.\n", - "Reduced MIP has 12 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.01 sec. (0.04 ticks)\n", - "Probing time = 0.00 sec. (0.01 ticks)\n", - "Clique table members: 14.\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 4 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.05 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 0.0000 7.0000 --- \n", - "* 0+ 0 2.0000 7.0000 250.00%\n", - " 0 0 5.7610 8 2.0000 5.7610 12 188.05%\n", - "* 0+ 0 3.0000 5.7610 92.03%\n", - "* 0+ 0 3.0000 --- \n", - " 0 0 cutoff 3.0000 27 --- \n", - "Elapsed time = 0.16 sec. (0.56 ticks, tree = 0.01 MB, solutions = 3)\n", - "\n", - "Clique cuts applied: 4\n", - "Cover cuts applied: 2\n", - "Flow cuts applied: 2\n", - "Mixed integer rounding cuts applied: 1\n", - "Lift and project cuts applied: 1\n", - "Gomory fractional cuts applied: 3\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.18 sec. (0.56 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.18 sec. (0.56 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "objective: 3\n", - "status: OPTIMAL_SOLUTION(2)\n", - " Low=1\n", - " Honey=1\n", - " Absent=1\n", - " Child_2=1\n", - " Child_3=1\n", - " Child_7=1\n" - ] - } - ], - "source": [ - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Integer Linear Optimization Models/TelevisionShow_Problem.ipynb b/Integer Linear Optimization Models/TelevisionShow_Problem.ipynb deleted file mode 100644 index c3e2950..0000000 --- a/Integer Linear Optimization Models/TelevisionShow_Problem.ipynb +++ /dev/null @@ -1,206 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "John White is the program scheduling manager for the\n", - "television channel CCFO. John would like to plan the schedule of television shows for\n", - "next Wednesday evening.\n", - "The table below lists nine shows under consideration. John must select exactly\n", - "five of these shows for the period from 8:00 p.m. to 10:30 p.m. next Wednesday\n", - "evening. For each television show, the estimated advertising revenue (in $ millions)\n", - "is provided. Furthermore, each show has been categorized into one or more of the\n", - "categories ­“Public Interest,” “Violent,” “Comedy,” and “Drama.” In the following\n", - "table, a 1 indicates that the show is in the corresponding category and a 0 indicates\n", - "it is not.\n", - "\n", - "John would like to determine a revenue-maximizing schedule of television shows\n", - "for next Wednesday evening. However, he must be mindful of the following\n", - "considerations:\n", - "\n", - "••The schedule must include at least as many shows that are categorized as public\n", - "interest as shows that are categorized as violent.\n", - "\n", - "••If John schedules “Loving Life,” then he must also schedule either “Jarred” or\n", - "­“Cincinnati Law” (or both).\n", - "\n", - "••John cannot schedule both “Loving Life” and “Urban Sprawl.”\n", - "\n", - "••If John schedules more than one show in the “Violent” category, he will lose an\n", - "­estimated $4 million in advertising revenues from family-oriented sponsors.\n", - "\n", - "a. Formulate a binary integer program that models the decisions John faces.\n", - "\n", - "b. Solve the model formulated in part (a). What is the optimal revenue?" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Show | Revenue | Public Interest | Violent | Comedy | Drama\n", - "# | | | | |\n", - "# Sam’s Place | $6 | 0 | 0 | 1 | 1\n", - "# Texas Oil | $10 | 0 | 1 | 0 | 1\n", - "# Cincinnati Law | $9 | 1 | 0 | 0 | 1\n", - "# Jarred | $4 | 0 | 1 | 0 | 1\n", - "# Bob & Mary | $5 | 0 | 0 | 1 | 0\n", - "# Chainsaw | $2 | 0 | 1 | 0 | 0\n", - "# Loving Life | $6 | 1 | 0 | 0 | 1\n", - "# Islanders | $7 | 0 | 0 | 1 | 0\n", - "# Urban Sprawl | $8 | 1 | 0 | 0 | 0\n", - "\n", - "shows = [\"Sam’s Place\", \"Texas Oil\", \"Cincinnati Law\", \"Jarred\", \"Bob & Mary\", \"Chainsaw\", \"Loving Life\", \"Islanders\", \"Urban Sprawl\"]\n", - "revenue = [6,10,9,4,5,2,6,7,8]\n", - "public_interest = [0,0,1,0,0,0,1,0,1]\n", - "violent = [0,1,0,1,0,1,0,0,0]\n", - "comedy = [1,0,0,0,1,0,0,1,0]\n", - "drama = [1,1,1,1,0,0,1,0,0]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "X = m.binary_var_list(shows)\n", - "XX_violent = m.binary_var(name = 'ViolentShowsMoreThanOne')\n", - "\n", - "public_interest_count = sum([public_interest[i] * X[i] for i in range(len(X))])\n", - "violent_count = sum([violent[i] * X[i] for i in range(len(X))])\n", - "m.add_constraint(public_interest_count >= violent_count)\n", - "\n", - "m.add_constraint(X[6] <= X[2] + X[3])\n", - "m.add_constraint(X[6] * X[8] <= 0)\n", - "\n", - "m.add_indicator(XX_violent, violent_count >= 2, 1)\n", - "m.add_indicator(XX_violent, violent_count <= 1, 0)\n", - "\n", - "m.maximize(sum([revenue[i] * X[i] for i in range(len(X))]) - (XX_violent * 4))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tried aggregator 2 times.\n", - "MIQCP Presolve eliminated 1 rows and 4 columns.\n", - "MIQCP Presolve modified 1 coefficients.\n", - "Aggregator did 1 substitutions.\n", - "Reduced MIQCP has 9 rows, 13 columns, and 27 nonzeros.\n", - "Reduced MIQCP has 6 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Reduced MIQCP has 1 quadratic constraints.\n", - "Presolve time = 0.02 sec. (0.03 ticks)\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 4 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.01 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - " 0 0 49.9443 0 57.0000 1 \n", - " 0 0 49.1803 2 Cone: 1 2 \n", - " 0 0 48.6679 3 Cone: 2 3 \n", - " 0 0 48.5700 3 Cone: 3 4 \n", - " 0 0 48.5025 3 Cone: 4 5 \n", - " 0 0 48.4856 3 Cone: 5 6 \n", - " 0 0 48.4767 3 Cone: 6 7 \n", - " 0 0 48.4741 3 Cone: 9 11 \n", - " 0 0 48.4741 3 Cone: 10 12 \n", - " 0 0 48.4741 3 Cone: 11 13 \n", - " 0 0 48.4741 3 48.4741 13 \n", - "* 0+ 0 45.0000 48.4741 7.72%\n", - " 0 0 cutoff 45.0000 18 --- \n", - "Elapsed time = 0.10 sec. (0.40 ticks, tree = 0.01 MB, solutions = 1)\n", - "\n", - "Cone linearizations applied: 3\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.12 sec. (0.40 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.12 sec. (0.40 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model2\n", - "objective: 45\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Sam’s Place=1\n", - "Texas Oil=1\n", - "Cincinnati Law=1\n", - "Jarred=1\n", - "Bob & Mary=1\n", - "Islanders=1\n", - "Urban Sprawl=1\n", - "ViolentShowsMoreThanOne=1\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/AdvertisingBudget_Problem.ipynb b/Linear Optimization Models/AdvertisingBudget_Problem.ipynb deleted file mode 100644 index 72d4251..0000000 --- a/Linear Optimization Models/AdvertisingBudget_Problem.ipynb +++ /dev/null @@ -1,86 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Advertising Budget Allocation.
\n", - "

The Sea Wharf Restaurant would like to determine the best way to allocate a\n", - " monthly advertising budget of $1,000 between newspaper advertising and radio\n", - " advertising. Management decided that at least 25% of the budget must be spent\n", - " on each type of media and that the amount of money spent on local newspaper\n", - " advertising must be at least twice the amount spent on radio advertising. A\n", - " marketing consultant developed an index that measures audience exposure per dollar\n", - " of advertising on a scale from 0 to 100, with higher values implying greater audience\n", - " exposure. If the value of the index for local newspaper advertising is 50 and the value\n", - " of the index for spot radio advertising is 80, how should the restaurant allocate its\n", - " advertising budget to maximize the value of total audience exposure?

" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets and Indicies

\n", - "\n", - "$\\mathcal{A}$: Ad types
\n", - "$a, a \\in \\mathcal{A}$: An index in set $\\mathcal{A}$
\n", - "\n", - "

Data

\n", - "\n", - "$b$: Budget
\n", - "$e_a, a \\in \\mathcal{A}$: Exposure for each Ad type
\n", - "\n", - "

Decision Variables

\n", - "\n", - "$x_a, a \\in \\mathcal{A}$: Budget per Ad type
\n", - "\n", - "

Linear Function

\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{maximize }} & \\; e^T x \\\\\n", - "\\text{subject to:} \\\\\n", - "& 1_nx^T \\leq b, n = ||x||\\\\ \n", - "& x_i \\geq 0.25b, \\forall i \\in \\mathcal{A}\\\\ \n", - "& 2x_{newspaper} \\geq x_{radio}\n", - "\\end{align*}\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/Bakery_Problem.ipynb b/Linear Optimization Models/Bakery_Problem.ipynb deleted file mode 100644 index 53782f7..0000000 --- a/Linear Optimization Models/Bakery_Problem.ipynb +++ /dev/null @@ -1,275 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bakery Problem ##\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
FlourEggSugar
We Have503020
Bagels Cost521
Muffins Cost442
\n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ProductRevenue
Bagels10
Muffins12
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sets\n", - "\n", - "$\\mathcal{B}$: Set of bakery choices, aka, Bagels or Muffins.\n", - "\n", - "$\\mathcal{I}$: Set of ingredients, aka, Flour, Eggs and Sugar.\n", - "\n", - "$\\mathcal{C}$: ???.\n", - "\n", - "## Indices\n", - "\n", - "$b$: Index for an arbitrary element in $\\mathcal{B}$.\n", - "\n", - "$i$: Index for an arbitrary element in $\\mathcal{I}$. \n", - "\n", - "## Data\n", - "\n", - "$t_i, i \\in \\mathcal{I}$: Amount available for ingredient $i$.\n", - "\n", - "$a_{bi}, b \\in \\mathcal{B}, i \\in \\mathcal{I}$: Amonut of ingredient $i$ needed to bake one dozen of $b$. \n", - "\n", - "$c_b, b \\in \\mathcal{B}$: Price of a dozen of $b$.\n", - "\n", - "## Decsion Variables\n", - "\n", - "$x_{b}, b \\in \\mathcal{B}$: Number of dozens to bake of $b$.\n", - "\n", - "## Linear Program\n", - "\n", - "\\begin{align*}\n", - "\\underset{\\{x_{b},\\; b \\in \\mathcal{B}\\}} {\\mathrm{maximize }} & \\; \\sum_{b \\in \\mathcal{B}}\\ c_b \\; x_{b} \\\\\n", - "\\text{subject to:} \\\\\n", - "& \\sum_{b \\in \\mathcal{B}} a_{bi} x_{b} \\leq t_i, \\forall i \\in \\mathcal{I} \\\\ \n", - "& x_b \\ge 0, \\forall b \\in \\mathcal{B}.\n", - "\\end{align*}\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "import numpy as np\n", - "\n", - "costs = np.array([[5, 2, 1], [4, 4, 2]])\n", - "\n", - "ItemToSell = np.array(['Bagels', 'Muffins'])\n", - "\n", - "revenue = [10, 12]\n", - "\n", - "Have = [50, 30, 20]\n", - "\n", - "m = Model(name = 'Bakery_problem')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "dv = np.array(m.continuous_var_list(ItemToSell, lb=0, ub=None, name = \"Pounds of %s\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[docplex.mp.LinearConstraint[](5Pounds of Bagels+4Pounds of Muffins,LE,50),\n", - " docplex.mp.LinearConstraint[](2Pounds of Bagels+4Pounds of Muffins,LE,30),\n", - " docplex.mp.LinearConstraint[](Pounds of Bagels+2Pounds of Muffins,LE,20)]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lhs = dv @ costs\n", - "\n", - "m.add_constraints([lhs[i] <= Have[i] for i in range(3)])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "exp = dv @ revenue\n", - "\n", - "m.maximize(exp)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'lp_Files\\\\Bakery_Problem.lp'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.export_as_lp(\"lp_Files\\Bakery_Problem.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-09 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 1 time.\n", - "LP Presolve eliminated 1 rows and 0 columns.\n", - "Reduced LP has 2 rows, 2 columns, and 4 nonzeros.\n", - "Presolve time = 0.00 sec. (0.00 ticks)\n", - "\n", - "Iteration log . . .\n", - "Iteration: 1 Dual infeasibility = 0.000000\n", - "Iteration: 2 Dual objective = 116.666667\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=116.667,values={Pounds of Bagels:6.." - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JobSolveStatus.OPTIMAL_SOLUTION\n", - "\n", - "objective: 116.667\n", - "status: OPTIMAL_SOLUTION(2)\n", - " \"Pounds of Bagels\"=6.667\n", - " \"Pounds of Muffins\"=4.167\n" - ] - } - ], - "source": [ - "print(m.solve_status)\n", - "print()\n", - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/Diet_Problem.ipynb b/Linear Optimization Models/Diet_Problem.ipynb deleted file mode 100644 index eda386c..0000000 --- a/Linear Optimization Models/Diet_Problem.ipynb +++ /dev/null @@ -1,227 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Base: \n", - "\n", - "Minimize $3x + 2y$ \n", - "\n", - "s.t\n", - "\n", - "$2x + y = 4$\n", - "\n", - "Higher Form\n", - "\n", - "1) Set / Inidices:\n", - " * $\\mathcal(F)$ = {1, 2}, f\n", - "2) Data\n", - " * $P_f, f ∈ F$\n", - " * $C_f, f ∈ F$\n", - "3) DUs\n", - " * $X_f, f ∈ F$\n", - "\n", - "___\n", - "\n", - "Maximize \n", - "$\\sum_{f ∈ \\mathcal F}(c_f x_f)$\n", - "\n", - "s.t\n", - "\n", - "$\\sum_{i\\in \\mathcal I} p_i \\; x_i = d$\n", - "..." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "m = Model(name = 'Diet_Problem')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Units of protien per pound, Price per pound\n", - "All_related_to_problem = {'Steak': [4, 3],\n", - " 'Peanut_Butter': [1, 1]}\n", - "\n", - "Protein_demand = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Decision Variables\n", - "x = m.integer_var_dict(All_related_to_problem.keys(), lb=0, ub=None, name = \"Pounds of %s used\")\n", - "# If we want half, or part of the pounds, we can change integer_var to continuoues_var" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "docplex.mp.LinearConstraint[](6Pounds of Steak used,LE,6)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x['Steak']*6 <= 6" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# Objective function // TRY TO DO MATRIX MULTIPLICATION\n", - "m.minimize(m.sum(x[i] * All_related_to_problem[i][1] for i in All_related_to_problem.keys()))" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "# Add constraint\n", - "m.add_constraint_(m.sum(x[i] * All_related_to_problem[i][0] for i in All_related_to_problem.keys()) == Protein_demand)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Diet_Problem.lp'" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Export the problem as LP\n", - "m.export_as_lp(\"Diet_Problem.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-06-15 | d64d5bd77\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 2 times.\n", - "MIP Presolve eliminated 1 rows and 2 columns.\n", - "MIP Presolve added 1 rows and 1 columns.\n", - "Aggregator did 1 substitutions.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.03 sec. (0.00 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.03 sec. (0.00 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.03 sec. (0.00 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=4,values={Pounds of Steak used:1,P.." - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JobSolveStatus.OPTIMAL_SOLUTION\n", - "\n", - "objective: 4\n", - "status: OPTIMAL_SOLUTION(2)\n", - " \"Pounds of Steak used\"=1\n", - " \"Pounds of Peanut_Butter used\"=1\n" - ] - } - ], - "source": [ - "print(m.solve_status)\n", - "print()\n", - "m.print_solution()\n", - "\n", - "####\n", - "# * Optimal Solution: [1, 0] \n", - "#\n", - "# * Optimal Objective Function Value: 3\n", - "# * Apply the optimal Solution numbers in the OF.\n", - "### " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/HotelRooms_Problem.ipynb b/Linear Optimization Models/HotelRooms_Problem.ipynb deleted file mode 100644 index bdc721c..0000000 --- a/Linear Optimization Models/HotelRooms_Problem.ipynb +++ /dev/null @@ -1,105 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Round Tree Manor is a hotel that provides two types of rooms with three rental classes: Super Saver, Deluxe, and Business. The profit per night for each type of room and rental class is as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RoomSuper saverDeluxeBusiness
Type I (Mountain View)30$35$--
Type II (Street View)20$30$40$
\n", - "\n", - "Round Tree’s management makes a forecast of the demand by rental class for each night in the future. A linear programming model developed to maximize profit is used to determine how many reservations to accept for each rental class. The demand forecast for a particular night is 130 rentals in the Super Saver class, 60 in the Deluxe class, and 50 in the Business class. Since these are the forecasted demands, Round Tree will take no more than these amounts of each reservation for each rental class. Round Tree has a limited number of each type of room. There are 100 Type I rooms and 120 Type II rooms.\n", - "\n", - "Formulate and solve a linear program to determine how many reservations to accept\n", - "in each rental class and how the reservations should be allocated to room types.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sets & Indicies\n", - "\n", - "$\\mathcal{R}, |\\mathcal{R}| = R$: Room types
\n", - "$\\mathcal{V}, |\\mathcal{V}| = V$: View types
\n", - "\n", - "$r, r \\in \\mathcal{R}$: An index in the set of Room Types
\n", - "$v, v \\in \\mathcal{V}$: An index in the set of View types
\n", - "\n", - "#### Data\n", - "\n", - "$P$: A matrix representing the profit for each room type and for each view type.
\n", - "$F$: A column vector representing the forcasted room for each room type.
\n", - "$A$: A column vector representing the profit for each view type.
\n", - "\n", - "$p_{rv} \\in P, r \\in \\mathcal{R}, v \\in \\mathcal{V}$: Profit per room type per view type.
\n", - "$f_r \\in F, r \\in \\mathcal{R}$: Forcasted rooms per room types.
\n", - "$a_v \\in A, v \\in \\mathcal{V}$: Available rooms per view types.
\n", - "\n", - "#### Decision Variables\n", - "\n", - "$X$: A matrix representing the number rooms to offer for each type of room and each view type
\n", - "$x_{rv} \\in X, r \\in \\mathcal{R}, v \\in \\mathcal{V}$: Number of rooms to offer per room type and view type
\n", - "\n", - "#### Linear function\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{maximize }} \\; {1_R^TXP^T1_R} \\\\\n", - "\\text{subject to:} \\\\\n", - "\\ ({X1_V})_i \\leq F_i, \\forall i \\in R; \\\\\n", - "\\ ({1_R^tX})_i \\leq A_i, \\forall i \\in V \\\\\n", - "\\end{align*}\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"1xR RxV VxR Rx1\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/InvestmentPortfolio_Problem.ipynb b/Linear Optimization Models/InvestmentPortfolio_Problem.ipynb deleted file mode 100644 index 292f5e4..0000000 --- a/Linear Optimization Models/InvestmentPortfolio_Problem.ipynb +++ /dev/null @@ -1,55 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
Investment Projected Rate of return
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets

\n", - "\n", - "$\\mathcal{O}$: Oil Investment
\n", - "$\\mathcal{S}$: Steel Investment
\n", - "$B$: Govermnet Bonds
\n", - "$o, o \\in \\mathcal{O}$: An index in set $\\mathcal{O}$
\n", - "$s, s \\in \\mathcal{S}$: An index in set $\\mathcal{S}$
\n", - "\n", - "

Data

\n", - "\n", - "$p, p \\in $ : Project amount
\n", - "\n", - "\n", - "Oil industry will recieve <= 50 K
\n", - "Steel industry will recieve <= 50 K
\n", - "Bonds >= 0.25 (Steel Industry)
\n", - " \n", - "X2 <= 0.6(Oil Industry)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/M&D_problem.ipynb b/Linear Optimization Models/M&D_problem.ipynb deleted file mode 100644 index 4ccab00..0000000 --- a/Linear Optimization Models/M&D_problem.ipynb +++ /dev/null @@ -1,109 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "M&D Chemicals produces two products that are sold as raw materials to companies that\n", - "manufacture bath soaps and laundry detergents. Based on an analysis of current inventory\n", - "levels and potential demand for the coming month, M&D’s management specified that\n", - "the combined production for products A and B must total at least 350 gallons. Separately,\n", - "a major customer’s order for 125 gallons of product A must also be satisfied. Product A\n", - "requires 2 hours of processing time per gallon, and product B requires 1 hour of processing\n", - "time per gallon. For the coming month, 600 hours of processing time are available. M&D’s\n", - "objective is to satisfy these requirements at a minimum total production cost. Production\n", - "costs are $2 per gallon for product A and $3 per gallon for product B.\n", - "___" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Product Aproduct B
Time Cost21
Money Cost23
\n", - "\n", - "We have: 600 hours
\n", - "We want: Minimize Money Cost
\n", - "Given That:\n", - "* A + B atleast 350\n", - "* A Atleast 125\n", - "___" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets

\n", - "\n", - "$\\mathcal{P}$: Products\n", - "\n", - "

Indicies

\n", - "\n", - "$p$: an index of the set $\\mathcal{P}$\n", - "\n", - "

Data

\n", - "\n", - "$t_p, p \\in P$: Time Cost per product.
\n", - "$m_p, p \\in P$: Money Cost per product.
\n", - "$a$ : The least number of products $\\mathcal{P}$ to create.
\n", - "$b$ : The least number of product $\\mathcal{P}_1$ to create.
\n", - "\n", - "

Decision Variables

\n", - "\n", - "$x_p, p \\in P$: Gallons to create\n", - "\n", - "

Linear Function

\n", - "\n", - "\n", - "

CHANGE TO INDINTITY MATRIX

\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{minimize }} \\; {m^Tg} \\\\\n", - "\\text{subject to:} \\\\\n", - "\\ {\\sum{g_i} \\geq a, {\\forall i \\in \\mathcal{I}}}, \\\\\n", - "\\ x_1 \\geq b \\\\\n", - "\\end{align*}\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/Notes.md b/Linear Optimization Models/Notes.md deleted file mode 100644 index 558dbfa..0000000 --- a/Linear Optimization Models/Notes.md +++ /dev/null @@ -1,81 +0,0 @@ -# LINEAR PROGRAMMING (Polynomial Time) -- Decision Variables --> Continuous -- Constraints --> Linear -- Objective Function --> Linear - -IF Decision VARIABLES ARE INTEGER, -THEN IT IS INTEGER LINEAR PROGRAMMING (Exponential Time) - -## BI problems: -- A- Stochastic -- B- Determenistic - - a- Linear - 1- Linear Programming - 2- Integer Linear Programming - - b- NonLinear - 1- Non Linear Programming - 1- Convex (Solvable) - 2- Non-Convex (Not Solvable) - 2- Integer Non Linear Programming - x- B INLP - -## Special Cases of solutions: -- A- Alternative Optimal Solution -- B- Infeasibility -- C- Unbounded - -## Convex (Linear) Combination: -Any point between 2 points given by: -Y = Lx1 + (1-L)x2, -where L is lambda, any number between 0-1 - -## Lets say: - -Maximize cx -s.t. Ax <= b - -## Change in A: -??? What happens - -## Change in b: -Each constraint has a shadow price: -- Which is the rate/ or amount of change on the objective value -for each [1] unit increase in the right hand side of a constraint. - -Binding constraint: -When the values of the optimal decision variable values in a constraint -it will exactly be on the limit of that constraint. e.g. - DV: a = 2, b = 4. - Constraint: 2a + 1b <= 8 ---> Binding Constraint - -- Non Binding Constraint will always have a shadow price of 0. -- Shadow prices of constraints where e.g. S>=0 or S<=20 is called "Reduced Cost of S". - -## Change in c: -Allowable Increase and Allowable Decrease: -Indicates the change in objective function coef for which the current solution -- will remain optimal. - -HMW: Apply Allowable Increase and Decrease + Check built-in commands for the ShadowPrice. - -- Variance of return is the risk -- The more you increase the projected return, the more risk you get. -- In portfolio selection: - - increase return without risk increasing above a certain threshold. -E.g: - - Minimize the risk expected - S.T. - Return >= A -or - - Maximize the return expected - S.T. - Risk <= A - -## There is a difference between optimal solution and optimal decision variable values. -Optimal objective function value is the evaluation for the optimal decision variables. -Solution refers to the optimal Decision variables values. - -## Generating An alternative optimal solution: -Doable by changing the Linear program such that it aims to maximize or minimize -other decision variables. While adding a constraint dictating that the solution -should be equal to the optimal solution. diff --git a/Linear Optimization Models/ParInc.ipynb b/Linear Optimization Models/ParInc.ipynb deleted file mode 100644 index 121122b..0000000 --- a/Linear Optimization Models/ParInc.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DepartmentStandardDeluxe
Cutting and Dyeing7/101
Sewing1/25/6
Finishing12/3
Packaging1/101/4
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DepartmentNumber of Hours
Cutting and Dyeing630
Sewing600
Finishing708
Inspection135
\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Sets & Matricies:

\n", - "\n", - "$\\mathcal{D}$: The departments.
\n", - "$\\mathcal{B}$: Types of bags.
\n", - "\n", - "

Indicies

\n", - "\n", - "$\\mathcal{d}$: An index in the set $\\mathcal{D}$
\n", - "$\\mathcal{b}$: An index in the set $\\mathcal{B}$
\n", - "\n", - "

Data

\n", - "\n", - "$a_d, d \\in \\mathcal{D} $: Hours available for each department.
\n", - "$n_{db}, d \\in \\mathcal{D}, b \\in \\mathcal{B}$: Hours needed for each type of bag in each department.
\n", - "$r_b, b \\in \\mathcal{B}$: Revenue for each type of bag.
\n", - "\n", - "

Decision Variables

\n", - "\n", - "$t_b, b \\in \\mathcal{B}$: Number of bags to create.
\n", - "\n", - "

Linear Program

\n", - "\n", - "\\begin{align*}\n", - "\\ {\\mathrm{maximize }} \\; {t^Tr} \\\\\n", - "\\text{subject to:} \\\\\n", - "\\ {nt} \\leq a \\\\\n", - "\\ t_b \\geq 0 ,\\; \\forall b \\in \\mathcal{B}\n", - "\\end{align*}" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "import numpy as np\n", - "\n", - "m = Model (name = 'ParInc')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "B = np.array(['Standard', 'Deluxe'])\n", - "a = np.array([630, 600, 708, 135])\n", - "n = np.array([[7/10, 1 ],\n", - " [1/2, 5/6],\n", - " [1, 2/3],\n", - " [1/10, 1/4]])\n", - "r = np.array([10, 12])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "t = np.array(m.continuous_var_list(B, lb = 0, ub = None))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[docplex.mp.LinearConstraint[](0.700Standard+Deluxe,LE,630),\n", - " docplex.mp.LinearConstraint[](0.500Standard+0.833Deluxe,LE,600),\n", - " docplex.mp.LinearConstraint[](Standard+0.667Deluxe,LE,708),\n", - " docplex.mp.LinearConstraint[](0.100Standard+0.250Deluxe,LE,135)]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.add_constraints([(nt_item <= a_item) for nt_item, a_item in zip(n@t, a)])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "m.maximize(t.T @ r)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-06-15 | d64d5bd77\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 1 time.\n", - "No LP presolve or aggregator reductions.\n", - "Presolve time = 0.00 sec. (0.00 ticks)\n", - "\n", - "Iteration log . . .\n", - "Iteration: 1 Dual infeasibility = 0.000000\n", - "Iteration: 2 Dual objective = 8424.000000\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=8424,values={Standard:540,Deluxe:2.." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JobSolveStatus.OPTIMAL_SOLUTION\n", - "\n", - "objective: 8424.000\n", - "status: OPTIMAL_SOLUTION(2)\n", - " Standard=540.000\n", - " Deluxe=252.000\n" - ] - } - ], - "source": [ - "print(m.solve_status)\n", - "print()\n", - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Linear Optimization Models/Transportation_Problem.ipynb b/Linear Optimization Models/Transportation_Problem.ipynb deleted file mode 100644 index 33b2c80..0000000 --- a/Linear Optimization Models/Transportation_Problem.ipynb +++ /dev/null @@ -1,41 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sets\n", - "set_plant = {'Cleaveland', 'Bedford', 'York'}\n", - "set_center = {'Boston', 'Chicago', 'St. Louis', 'Lexington'}\n", - "\n", - "# Data\n", - "plant_supplies = [5000, 6000, 2500]\n", - "distrubution_centers = [6000, 4000, 2000, 1500]\n", - "transportation_costs = [[3, 2, 7, 6], \n", - " [6, 5, 2, 3],\n", - " [2, 5, 4, 5]]\n", - "\n", - "# Decision Variable\n", - "# X_{s, d}\n", - "\n", - "# Maximize_{X_{sd}, s \\in S, d \\in D} Sum_{s \\in S}(Sum_{d \\in D}(C_{sd}X_{sd}))\n", - "\n", - "# Subject to\n", - "\n", - "# Sum_{d \\in D}(X_{sd}) <= P_s, \\forall s \\in S\n", - "# Sum_{s\\in S}(X_{sd}) <= m_d, \\forall d \\in D\n", - "# X_sd >= 0, \\forall\n", - "# " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/HWs/HW1.ipynb b/Madi/HWs/HW1.ipynb deleted file mode 100644 index 3c32a1f..0000000 --- a/Madi/HWs/HW1.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Mohammed Madi 20200386" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Problem Statement**\n", - "\n", - "A company has ten employees, seven machines, and thirty jobs to be completed. The time required \n", - "to complete job 𝑗 by employee 𝑒 using machine 𝑚 is given and denoted by $𝑡_{𝑗𝑒𝑚}$. An employee can \n", - "only perform one job at a time. Similarly, a machine can only execute one job at a time. A job \n", - "cannot be divided among employees. Also, a job cannot be divided between machines. Develop a \n", - "mathematical formulation that can be used to obtain the optimal job-employee-machine \n", - "assignment if the company wants to minimize the total time spent by all machines to complete the \n", - "seven jobs. Generate different scenarios and, using Cplex, implement and solve your formulation \n", - "for these scenarios.\n", - "\n", - "## **Sets**\n", - "\n", - "$\\mathcal{E}$: Set of employees that work at the company.\n", - "\n", - "$\\mathcal{M}$: Set of Machines that can be used.\n", - "\n", - "$\\mathcal{J}$: Set of Jobs to be completed.\n", - "\n", - "## **Indices**\n", - "\n", - "$e$: Index for an arbitrary element in $\\mathcal{E}$.\n", - "\n", - "$m$: Index for an arbitrary element in $\\mathcal{M}$.\n", - "\n", - "$j$: Index for an arbitrary element in $\\mathcal{J}$\n", - "\n", - "## **Data**\n", - "\n", - "$t_{jem}, j \\in \\mathcal{J}, e \\in \\mathcal{E}, m \\in \\mathcal{M}$, The amount of time needed to completed job $j$ by employee $e$ using machine $m$.\n", - "\n", - "\n", - "## **Decision Variables**\n", - "\n", - "$x_{jem}, j \\in \\mathcal{J}, e \\in \\mathcal{E}, m \\in \\mathcal{M}$, Decide which employee should do which job using which machine.\n", - "\n", - "## **Linear Program**\n", - "\n", - "**Objective Function**\n", - "\\begin{align*}\n", - "\\mathrm{minimize} \\sum_{j \\in \\mathcal{J}}\\sum_{e \\in \\mathcal{E}}\\sum_{m \\in \\mathcal{M}} x_{jem}t_{jem}\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\\begin{gather}\n", - "\\sum_{e \\in \\mathcal{E}} \\sum_{m \\in \\mathcal{M}} x_{jem} = 1, \\forall j \\in \\mathcal{J}, \\text{ Each job can only be done by 1 employee and 1 machine.}\\\\\n", - "x_{jem} \\in \\{0, 1\\}, \\forall j \\in \\mathcal{J}, \\forall e \\in \\mathcal{E}, \\forall m \\in \\mathcal{M}, \\text{Binary constraint.} \\\\\n", - "\\end{gather}\n", - "\n", - "## **Interpertation**\n", - "\n", - "The assignment matrix is telling us which employee should use which machine to do the task, since we only care about total time, if the same employee gets multiple jobs he can perform them in any order he can.\n", - "\n", - "I wanted to try to write the other objective function in which we care about time to finish, but I didnt have the time to do so.\n", - "\n", - "(:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name = \"HW1\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from random import randint\n", - "# Generating random data\n", - "num_employees = 10\n", - "num_machines = 7\n", - "num_jobs = 30\n", - "\n", - "t = [[[randint(1, 100) for m in range(num_machines)] for e in range(num_employees)] for j in range(num_jobs)]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Decision Variable\n", - "x = model.binary_var_cube(num_jobs, num_employees, num_machines, name = \"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Objective Function\n", - "model.minimize(model.sum(t[j][e][m] * x[(j, e, m)] for j in range(num_jobs) for e in range(num_employees) for m in range(num_machines)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding constraints\n", - "for j in range(num_jobs):\n", - " model.add_constraint(model.sum(x[(j, e, m)] for e in range(num_employees) for m in range(num_machines)) == 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'HW1.lp'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"HW1.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 1206.000000 after 0.00 sec. (0.12 ticks)\n", - "Found incumbent of value 59.000000 after 0.00 sec. (0.18 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 30 rows and 2100 columns.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.02 sec. (0.71 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.02 sec. (0.98 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.02 sec. (0.98 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=59,values={x_0_0_3:1,x_1_9_3:1,x_2.." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Employee 0 is assigned to Job 0 on Machine 3 with time 1\n", - "Employee 0 is assigned to Job 2 on Machine 6 with time 1\n", - "Employee 0 is assigned to Job 3 on Machine 3 with time 2\n", - "Employee 0 is assigned to Job 14 on Machine 0 with time 2\n", - "Employee 0 is assigned to Job 16 on Machine 0 with time 2\n", - "Employee 0 is assigned to Job 17 on Machine 4 with time 1\n", - "Employee 1 is assigned to Job 4 on Machine 5 with time 5\n", - "Employee 1 is assigned to Job 5 on Machine 2 with time 2\n", - "Employee 1 is assigned to Job 6 on Machine 5 with time 2\n", - "Employee 1 is assigned to Job 12 on Machine 2 with time 1\n", - "Employee 1 is assigned to Job 23 on Machine 4 with time 2\n", - "Employee 2 is assigned to Job 7 on Machine 0 with time 1\n", - "Employee 2 is assigned to Job 8 on Machine 3 with time 1\n", - "Employee 2 is assigned to Job 19 on Machine 1 with time 8\n", - "Employee 3 is assigned to Job 21 on Machine 5 with time 2\n", - "Employee 3 is assigned to Job 24 on Machine 0 with time 1\n", - "Employee 3 is assigned to Job 25 on Machine 2 with time 2\n", - "Employee 4 is assigned to Job 26 on Machine 2 with time 1\n", - "Employee 5 is assigned to Job 18 on Machine 6 with time 1\n", - "Employee 6 is assigned to Job 10 on Machine 1 with time 1\n", - "Employee 6 is assigned to Job 13 on Machine 4 with time 4\n", - "Employee 7 is assigned to Job 9 on Machine 0 with time 1\n", - "Employee 7 is assigned to Job 11 on Machine 5 with time 1\n", - "Employee 7 is assigned to Job 22 on Machine 6 with time 4\n", - "Employee 7 is assigned to Job 29 on Machine 3 with time 1\n", - "Employee 9 is assigned to Job 1 on Machine 3 with time 1\n", - "Employee 9 is assigned to Job 15 on Machine 1 with time 1\n", - "Employee 9 is assigned to Job 20 on Machine 1 with time 3\n", - "Employee 9 is assigned to Job 27 on Machine 0 with time 2\n", - "Employee 9 is assigned to Job 28 on Machine 5 with time 2\n", - "Sum of all values in assignment matrix: 30.0\n", - "Min time: 59.0\n", - "Solution is correct!\n" - ] - } - ], - "source": [ - "# verifying the solution\n", - "obj_value = model.objective_value\n", - "assignment_matrix = [[[x[(j, e, m)].solution_value for j in range(num_jobs)] for e in range(num_employees)] for m in range(num_machines)]\n", - "\n", - "sum_vals = 0\n", - "for e in range(num_employees):\n", - " for j in range(num_jobs):\n", - " for m in range(num_machines):\n", - " if assignment_matrix[m][e][j] == 1:\n", - " print(f\"Employee {e} is assigned to Job {j} on Machine {m} with time {t[j][e][m]}\")\n", - " sum_vals += t[j][e][m]\n", - " assert t[j][e][m] == min([t[j][e_][m_] for e_ in range(num_employees) for m_ in range(num_machines)])\n", - "\n", - "\n", - "assigned_jobs = sum(sum(sum(row) for row in matrix) for matrix in assignment_matrix)\n", - "assert assigned_jobs == num_jobs\n", - "print(\"Sum of all values in assignment matrix:\", assigned_jobs)\n", - "\n", - "assert sum_vals == obj_value\n", - "print(\"Min time:\", obj_value)\n", - "print(\"Solution is correct!\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/HWs/HW2.ipynb b/Madi/HWs/HW2.ipynb deleted file mode 100644 index 36900ff..0000000 --- a/Madi/HWs/HW2.ipynb +++ /dev/null @@ -1,497 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "image.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve problem on Page 740 of the book but make it binary instead.\n", - "## **Location Problem**\n", - "Let us consider the case of LaRosa Machine Shop (LMS). LMS is studying where to locate \n", - "its tool bin facility on the shop floor. The locations of the five production stations appear in \n", - "Figure 14.9. In an attempt to be fair to the workers in each of the production stations, man\u0002agement has decided to try to find the position of the tool bin that would minimize the sum \n", - "of the distances from the tool bin to the five production stations. We define the following \n", - "decision variables:\n", - "\n", - "X = horizontal location of the tool bin\n", - "\n", - "Y = vertical location of the tool bin\n", - "\n", - "![image.png](attachment:image.png)\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StationLocation XLocation Y
Fabrication14
Paint12
Subassembly 12.52
Subassembly 235
Assembly44
\n", - "\n", - "## **Solution 1**\n", - "**Use calculus**\n", - "I had to brush up my calc skills a bit Here are some good resources for that:\n", - "\n", - "https://www.youtube.com/watch?v=Hg38kfK5w4E\n", - "\n", - "https://calcworkshop.com/partial-derivatives/extrema-multivariable-functions/\n", - "\n", - "https://math.libretexts.org/Bookshelves/Calculus/Vector_Calculus_(Corral)/02%3A_Functions_of_Several_Variables/2.05%3A_Maxima_and_Minima" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "data = [(1, 4), (1, 2), (2.5, 2), (3, 5), (4, 4)]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def f(x, y, data):\n", - " x = np.atleast_2d(x)\n", - " y = np.atleast_2d(y)\n", - " sum_dist = np.zeros(x.shape)\n", - " for x_i, y_i in data:\n", - " sum_dist += np.sqrt((x - x_i)**2 + (y - y_i)**2)\n", - " return sum_dist" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# def not GPT written or anything, i can do second year data engineering (:\n", - "def plot3d(x, y):\n", - " X, Y = np.meshgrid(x, y)\n", - " Z = f(X, Y, data)\n", - " fig = plt.figure()\n", - " ax = fig.add_subplot(111, projection='3d')\n", - " surf = ax.plot_surface(X, Y, Z, cmap='viridis')\n", - " fig.colorbar(surf)\n", - " ax.set_xlabel('X coordinate')\n", - " ax.set_ylabel('Y coordinate')\n", - " ax.set_zlabel('Z value')\n", - " plt.title('3D Surface Plot')\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 6, 100)\n", - "y = np.linspace(0, 6, 100)\n", - "plot3d(x, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So this is what our function looks like, we can see that a minimum does exist within our bounds $x \\in [0, 6], y \\in [0, 6]$\n", - "\n", - "now lets do some calc\n", - "\n", - "\\begin{align*}\n", - "f(x, y) = \\sum_{(x_i, y_i) \\in \\mathcal{S}} \\sqrt{(x - x_i)^2 + (y - y_i)^2}\\\\\n", - "\\frac{\\partial f}{\\partial x} = \\sum_{(x_i, y_i) \\in \\mathcal{S}} \\frac{2(x - x_i)} {2 \\sqrt{(x - x_i)^2 + (y - y_i)^2}}\\\\\n", - "\\frac{\\partial f}{\\partial y} = \\sum_{(x_i, y_i) \\in \\mathcal{S}} \\frac{2(y - y_i)} {2 \\sqrt{(x - x_i)^2 + (y - y_i)^2}}\\\\\n", - "\\end{align*}\n", - "\n", - "So now we have a very funny system of equations, we use numerical methods to solve in python." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solution for (x, y): [2.22989218 3.34881129]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from scipy.optimize import fsolve\n", - "\n", - "def equations(p):\n", - " x, y = p\n", - " sum_x = sum((x - xi) / np.sqrt((x - xi)**2 + (y - yi)**2) for xi, yi in data)\n", - " sum_y = sum((y - yi) / np.sqrt((x - xi)**2 + (y - yi)**2) for xi, yi in data)\n", - " return (sum_x, sum_y)\n", - "\n", - "initial_guess = (0, 0)\n", - "\n", - "solution = fsolve(equations, initial_guess)\n", - "\n", - "print(\"Solution for (x, y):\", solution)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8, 6))\n", - "plt.scatter(*zip(*data), color='blue', label='Data Points')\n", - "plt.scatter(*solution, color='red', label='Solution Point', zorder=5)\n", - "plt.xlabel('X Coordinate')\n", - "plt.ylabel('Y Coordinate')\n", - "plt.title('Data Points and Calculated Solution')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.xlim(0, 6)\n", - "plt.ylim(0, 6)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ofcourse if I were to fully work through this i would need to check the boundary points of our range, but we can see from the first plot that the min isnt there so am not going to do it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Solution 2**\n", - "Solve this a binary Linear programming problem.\n", - "## **Idea**\n", - "Turn this space into a 6 x 6 grid of squares, then precompute the distance between the center of each square to each of the 5 locations, then just use a 6 x 6 binary var grid to pick which ever location minimizes the sum of distances.\n", - "\n", - "## **Sets**\n", - "\n", - "$\\mathcal{X}$: Set of x coordinates.\n", - "\n", - "$\\mathcal{Y}$: Set of y coordinates.\n", - "\n", - "$\\mathcal{P}$: Set of Stations.\n", - "\n", - "$\\mathcal{C} = \\{(x, y), x \\in \\mathcal{X}, y \\in \\mathcal{Y}\\}$: Coordinates for each point we want to test.\n", - "\n", - "\n", - "## **Indecies**\n", - "$p \\in \\mathcal{P}$: Element in $\\mathcal{P}$.\n", - "\n", - "$(x, y) \\in \\mathcal{C}$: Coordinates in $\\mathcal{C}$\n", - "\n", - "## **Data**\n", - "$s_p,p \\in \\mathcal{P}$: Coordinates for each station $p$.\n", - "\n", - "$d_{xyp}, (x, y) \\in \\mathcal{C}, p \\in \\mathcal{P}$, Distance from each point $(x, y)$ to each station $p$.\n", - "\n", - "## **Decision Variables**\n", - "\n", - "$z_{xy}, (x, y) \\in \\mathcal{C}$: Binary var representing whether to use location $(x, y)$.\n", - "\n", - "## **Formulation**\n", - "\n", - "**Objective Function**\n", - "\\begin{align*}\n", - "\\mathrm{Min} \\sum_{(x, y) \\in \\mathcal{C}} z_{xy} (\\sum_{p \\in \\mathcal{P}} d_{xyp})\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\n", - "\\begin{gather}\n", - "\\sum_{(x, y) \\in \\mathcal{C}} z_{xy} = 1\\\\\n", - "z_{xy} \\in \\{0, 1\\}, \\forall (x, y) \\in \\mathcal{C}\n", - "\\end{gather}\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name = \"Location Problem\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "def dist(p1, p2):\n", - " x1, y1 = p1\n", - " x2, y2 = p2\n", - " return math.sqrt((x1 - x2)**2 + (y1 - y2)**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "## Data\n", - "data = [(1, 4), (1, 2), (2.5, 2), (3, 5), (4, 4)]\n", - "X = list(range(6))\n", - "Y = list(range(6))\n", - "P = list(range(5))\n", - "\n", - "C = [(x, y) for x in X for y in Y]\n", - "d = {(x, y, p) : dist((x, y), data[p]) for x in X for y in Y for p in P}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def plotpoints(points):\n", - " x, y = zip(*points)\n", - " # Create a scatter plot\n", - " plt.figure(figsize=(6, 4))\n", - " plt.scatter(x, y, color='blue') # Scatter plot: x vs y\n", - " plt.title('Scatter Plot of Points')\n", - " plt.xlabel('X Coordinate')\n", - " plt.ylabel('Y Coordinate')\n", - " plt.grid(True) # Optional: adds a grid\n", - " plt.axis('equal') # Optional: ensures the scale of x and y axes are equal\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plotpoints(C)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "z = model.binary_var_dict(C, name = \"z\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "model.minimize(model.sum(z[x, y] * model.sum(d[x, y, p] for p in P) for x, y in C))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "docplex.mp.LinearConstraint[](z_0_0+z_0_1+z_0_2+z_0_3+z_0_4+z_0_5+z_1_0+z_1_1+z_1_2+z_1_3+z_1_4+z_1_5+z_2_0+z_2_1+z_2_2+z_2_3+z_2_4+z_2_5+z_3_0+z_3_1+z_3_2+z_3_3+z_3_4+z_3_5+z_4_0+z_4_1+z_4_2+z_4_3+z_4_4+z_4_5+z_5_0+z_5_1+z_5_2+z_5_3+z_5_4+z_5_5,EQ,1)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.add_constraint(model.sum(z[x, y] for x in X for y in Y) == 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'location.lp'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"location.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 21.048542 after 0.00 sec. (0.00 ticks)\n", - "Found incumbent of value 8.418597 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 1 rows and 36 columns.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.00 sec. (0.02 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.00 sec. (0.02 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=8.4186,values={z_2_3:1})" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8.418597068495664\n", - "[(2, 3)]\n" - ] - } - ], - "source": [ - "obj = model.objective_value\n", - "assigment = [(x, y) for x in X for y in Y if z[x, y].solution_value == 1]\n", - "print(obj)\n", - "print(assigment)\n", - "# yayyyyy this is what we expected from the calc solve" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/ILP/BankTeller_Problem.ipynb b/Madi/ILP/BankTeller_Problem.ipynb deleted file mode 100644 index 8addfd3..0000000 --- a/Madi/ILP/BankTeller_Problem.ipynb +++ /dev/null @@ -1,339 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Bank Teller Problem**\n", - "The Northside Bank is working to develop an efficient work\n", - "schedule for full-time and part-time tellers. The schedule must provide for efficient\n", - "operation of the bank, including adequate customer service, employee breaks, and so\n", - "on. On Fridays, the bank is open from 9:00 a.m. to 7:00 p.m. The number of tellers\n", - "necessary to provide adequate customer service during each hour of operation is summarized as follows:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
TimeNo. of Tellers
9:00 a.m. – 10:00 a.m.6
10:00 a.m. – 11:00 a.m.4
11:00 a.m. – Noon8
Noon – 1:00 p.m.10
1:00 p.m. – 2:00 p.m.9
2:00 p.m. – 3:00 p.m.6
3:00 p.m. – 4:00 p.m.4
4:00 p.m. – 5:00 p.m.7
5:00 p.m. – 6:00 p.m.6
6:00 p.m. – 7:00 p.m.6
\n", - "\n", - "Each full-time employee starts on the hour and works a 4-hour shift, followed by a\n", - "1-hour break and then a 3-hour shift. Part-time employees work one 4-hour shift beginning on the hour. Considering salary and fringe benefits, full-time employees cost the\n", - "bank $15 per hour ($105 a day), and part-time employees cost the bank $8 per hour\n", - "($32 per day).\n", - "\n", - "Formulate an integer programming model that can be used to develop a schedule\n", - "that will satisfy customer service needs at a minimum employee cost.\n", - "\n", - "\n", - "## **Sets**\n", - "$\\mathcal{T}$: Set of all shifts.\n", - "\n", - "$\\mathcal{F}$: Full time employee start times.\n", - "\n", - "$\\mathcal{P}$: Part time employee start times.\n", - "\n", - "## **Indecies**\n", - "\n", - "$t \\in \\mathcal{T}$: Index of a shit.\n", - "\n", - "$f \\in \\mathcal{F}$: Index of full time employee start time.\n", - "\n", - "$p \\in \\mathcal{P}$: Index of part time employee start time.\n", - "\n", - "## **Data**\n", - "\n", - "$n_{t}, t \\in \\mathcal{T}$: Number of tellers that need to be working during shift $t$.\n", - "\n", - "$w_{ft}, f \\in \\mathcal{F}, t \\in \\mathcal{T}$ binary number indicating whether full time employee starting at $f$ is working at $t$.\n", - "\n", - "$c_{pt}, p \\in \\mathcal{F}, t \\in \\mathcal{T}$ binary number indicating whether part time employee starting at $p$ is working at $t$.\n", - "\n", - "$d$: Daily salary for full time employee.\n", - "\n", - "$e$: Daily salary for part time employee.\n", - "\n", - "## **Decision Variables**\n", - "$x_{f}, f \\in \\mathcal{F}$: Number of full time employees to start at time $f$.\n", - "\n", - "$y_{p}, p \\in \\mathcal{P}$: Number of part time employees to start at time $p$.\n", - "\n", - "## **Formulation**\n", - "**Objective Function**\n", - "\\begin{align*}\n", - "\\mathrm{Min}\\sum_{f \\in \\mathcal{F}}x_{f} + \\sum_{p \\in \\mathcal{P}} y_{p}\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\\begin{gather}\n", - "\\sum_{f \\in \\mathcal{F}}x_{f}w_{ft} + \\sum_{p \\in \\mathcal{P}} y_{p}c_{pt} \\ge n_{t}, \\forall t \\in \\mathcal{T}\\\\\n", - "x_{f} \\ge 0, f \\in \\mathcal{F} \\\\\n", - "x_{f} \\in \\mathbb{Z}, f \\in \\mathcal{F} \\\\\n", - "y_{p} \\ge 0, p \\in \\mathcal{P} \\\\\n", - "y_{p} \\in \\mathbb{Z}, p \\in \\mathcal{P} \\\\\n", - "\\end{gather}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name = \"BankTellers\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "T = list(range(10))\n", - "F = list(range(3))\n", - "P = list(range(7))\n", - "\n", - "n = [6, 4, 8, 10, 9, 6, 4, 7, 6, 6]\n", - "\n", - "w = [\n", - " [1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n", - " [0, 1, 1, 1, 1, 0, 1, 1, 1, 0],\n", - " [0, 0, 1, 1, 1, 1, 0, 1, 1, 1]\n", - "]\n", - "\n", - "c = [\n", - " [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n", - " [0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n", - " [0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n", - " [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n", - " [0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n", - " [0, 0, 0, 0, 0, 0, 1, 1, 1, 1]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "x = model.integer_var_list(len(F), lb = 0, name = \"x\")\n", - "y = model.integer_var_list(len(P), lb = 0, name = \"y\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model.minimize(model.sum(x[f] for f in F) + model.sum(y[p] for p in P))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "for t in T:\n", - " model.add_constraint(model.sum(w[f][t] * x[f] for f in F) + model.sum(c[p][t] * y[p] for p in P) >= n[t])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'BankTellers.lp'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"BankTellers.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 25.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 2 rows and 0 columns.\n", - "Reduced MIP has 8 rows, 10 columns, and 41 nonzeros.\n", - "Reduced MIP has 0 binaries, 10 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.02 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "Reduced MIP has 8 rows, 10 columns, and 41 nonzeros.\n", - "Reduced MIP has 0 binaries, 10 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.02 ticks)\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 16 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.01 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 25.0000 0.0000 100.00%\n", - "* 0+ 0 15.0000 0.0000 100.00%\n", - " 0 0 cutoff 15.0000 15.0000 9 0.00%\n", - " 0 0 cutoff 15.0000 15.0000 9 0.00%\n", - "Elapsed time = 0.02 sec. (0.07 ticks, tree = 0.01 MB, solutions = 2)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.02 sec. (0.07 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.02 sec. (0.07 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=15,values={x_0:6,x_1:3,x_2:6})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.0\n", - "9.0\n", - "15.0\n", - "15.0\n", - "9.0\n", - "12.0\n", - "9.0\n", - "15.0\n", - "9.0\n", - "6.0\n", - "Objective value: 15.0\n", - "[6.0, 3.0, 6.0]\n", - "[0, 0, 0, 0, 0, 0, 0]\n" - ] - } - ], - "source": [ - "obj = model.objective_value\n", - "assignmentx = [x[f].solution_value for f in F]\n", - "assignmenty = [y[p].solution_value for p in P]\n", - "\n", - "for t in T:\n", - " assert sum(w[f][t] * assignmentx[f] for f in F) + sum(c[p][t] * assignmenty[p] for p in P) >= n[t]\n", - "\n", - "# turns out part timers are uselss (:\n", - "print(\"Objective value: \", obj)\n", - "print(assignmentx)\n", - "print(assignmenty)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/ILP/ComponentOrdering_Problem.ipynb b/Madi/ILP/ComponentOrdering_Problem.ipynb deleted file mode 100644 index d7e796c..0000000 --- a/Madi/ILP/ComponentOrdering_Problem.ipynb +++ /dev/null @@ -1,81 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Morgan Inc. is planning the purchase of one of the component\n", - "parts it needs for its finished product. The anticipated demands for the component for\n", - "the next 12 periods are shown in the following table.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Period123456789101112
Demand2020304014036050054046080020
\n", - "\n", - " The cost to order the component\n", - "(labor, shipping, and paperwork) is $150. The cost to hold these components in inventory is $1 per component per period. The price of the component is expected to remain\n", - "stable at $12 per unit for the next 12 periods, and no quantity discounts are available.\n", - "The maximum order size is 1,000 units.\n", - "Formulate a model to minimize the total cost of satisfying Morgan Inc.’s demand\n", - "for this component." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "9682219fa65ec76ab0a712f3cc506ba5eb9451d445ad0b772c7739d17e7a6e46" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/ILP/Locating_Police_Substations.ipynb b/Madi/ILP/Locating_Police_Substations.ipynb deleted file mode 100644 index e085cdd..0000000 --- a/Madi/ILP/Locating_Police_Substations.ipynb +++ /dev/null @@ -1,238 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Locating Police Substations**\n", - "Grave City is considering the relocation of several police substations to obtain better enforcement in high-crime areas. The locations under consideration together with the areas that can be covered from these locations are given in the following table:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Potential Locations for SubstationsAreas Covered
A1, 5, 7
B1, 2, 5, 7
C1, 3, 5
D2, 4, 5
E3, 4, 6
F4, 5, 6
G1, 5, 6, 7
\n", - "\n", - "### **Sets**\n", - "$\\mathcal{P}$: Set of police substations.\n", - "\n", - "$\\mathcal{A}$: Set of Areas to cover.\n", - "\n", - "### **Indecies**\n", - "$p \\in \\mathcal{P}$: index of police substation.\n", - "\n", - "$a \\in \\mathcal{A}$: index of area to cover.\n", - "\n", - "### **Data**\n", - "$c_{ap}, a \\in \\mathcal{A}, p \\in \\mathcal{P}$: a binary value indicating whether area $a$ is covered by substation $p$.\n", - "\n", - "### **Decision Vars**\n", - "$x_{p}, p \\in \\mathcal{P}$, Binary variable indicating whether to use location $p$ or not. \n", - "\n", - "### **Formulation**\n", - "**Objective function**\n", - "\\begin{align*}\n", - "\\mathrm{Min} \\sum_{p \\in \\mathcal{P}} x_{p}\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\\begin{gather}\n", - "\\sum_{p \\in \\mathcal{P}} c_{ap}x_{p} \\ge 1, \\forall a \\in \\mathcal{A} \\\\\n", - "x_{p} \\in \\{1, 0\\}, \\forall p \\in \\mathcal{P}\n", - "\\end{gather}" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name='Substations')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "P = list(range(7))\n", - "A = list(range(7))\n", - "\n", - "c = [\n", - " [1, 0, 0, 0, 1, 0, 1],\n", - " [1, 1, 0, 0, 1, 0, 1],\n", - " [1, 0, 1, 0, 1, 0, 0],\n", - " [0, 1, 0, 1, 1, 0, 0],\n", - " [0, 0, 1, 1, 0, 1, 0],\n", - " [0, 0, 0, 1, 1, 1, 0],\n", - " [1, 0, 0, 0, 1, 1, 1]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "x = model.binary_var_list(len(P), name = \"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model.minimize(model.sum(x[p] for p in P))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "for a in A:\n", - " model.add_constraint(model.sum(c[p][a] * x[p] for p in P) >= 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'substation.lp'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"substation.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 7.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 7 rows and 6 columns.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.00 sec. (0.01 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.00 sec. (0.01 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.00 sec. (0.01 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=2,values={x_1:1,x_4:1})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objective value: 2.0\n", - "[0, 1.0, 0, 0, 1.0, 0, 0]\n" - ] - } - ], - "source": [ - "obj = model.objective_value\n", - "assignment = [x[p].solution_value for p in P]\n", - "\n", - "print(\"Objective value: \", obj)\n", - "print(assignment)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/ILP/Nurse_Scheduling.ipynb b/Madi/ILP/Nurse_Scheduling.ipynb deleted file mode 100644 index 8fa0bbb..0000000 --- a/Madi/ILP/Nurse_Scheduling.ipynb +++ /dev/null @@ -1,266 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Nurse Scheduling**\n", - "Hospital administrators must schedule nurses so that the hospital’s patients are provided adequate care. At the same time, careful attention must be paid to keeping costs down. From historical records, administrators can project the minimum number of nurses required to be on hand for various times of day and days of the week. \n", - "\n", - "The objective is to find the minimum total number of nurses required to provide adequate care.Nurses start work at the beginning of one of the four-hour shifts given below (except for shift 6) and work for 8 consecutive hours. Hence, possible start times are the start of shifts 1 through 5. Also, assume that the projected required number of nurses factors in time for each nurse to have a meal break.\n", - "\n", - "Formulate and solve the nurse scheduling problem as an integer program for one day for the data given below.\n", - "Hint: Note that exceeding the minimum number of needed nurses in each shift is acceptable so long as the total number of nurses over all shifts is minimized.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Time No. of Nurses
12:00 a.m. – 4:00 a.m.10
4:00 a.m. – 8:00 a.m.24
8:00 a.m. – 12:00 p.m.18
12:00 p.m. – 4:00 p.m.10
4:00 p.m. – 8:00 p.m.23
8:00 p.m. – 12:00 a.m.17
\n", - "\n", - "## **Sets**\n", - "$\\mathcal{T}$: Set of shifts.\n", - "\n", - "$\\mathcal{S}$: Set of nurse start times.\n", - "\n", - "## **Indecies**\n", - "$t \\in \\mathcal{T}$: index of shift in shifts.\n", - "\n", - "$s \\in \\mathcal{S}$ index of start time in possible nurse start times.\n", - "\n", - "## **Data**\n", - "$n_{t}, t \\in \\mathcal{T}$, Amount of needed nurses at each time step.\n", - "\n", - "$w_{st}, t \\in \\mathcal{T}, s \\in \\mathcal{S}$ binary value indicating whether the nurses who started at shift $s$ is working during shift $t$.\n", - "## **Decision Variables**\n", - "$x_s, s \\in \\mathcal{S}$ Number of nurses starting at time $s$.\n", - "## **Formulation**\n", - "**Objective function**\n", - "\\begin{align*}\n", - "\\mathrm{Min} \\sum_{s \\in \\mathcal{S}} x_{s}\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\\begin{gather}\n", - "\\sum_{s \\in \\mathcal{S}}x_{s} w_{st} \\ge n_{t}, \\forall t \\in \\mathcal{T} \\\\\n", - "x_{s} \\ge 0, \\forall s \\in \\mathcal{S} \\\\\n", - "x_{s} \\in \\mathbb{Z}, \\forall s \\in \\mathcal{S}\\\\\n", - "\\end{gather}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name = \"Nurse Scheduling\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Data\n", - "T = list(range(6))\n", - "S = list(range(5))\n", - "\n", - "n = [10, 24, 28, 10, 23, 17]\n", - "w = [\n", - " [1, 1, 0, 0, 0, 0],\n", - " [0, 1, 1, 0, 0, 0],\n", - " [0, 0, 1, 1, 0, 0],\n", - " [0, 0, 0, 1, 1, 0],\n", - " [0, 0, 0, 0, 1, 1]\n", - "]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "x = model.integer_var_list(len(S), name = \"x\", lb = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model.minimize(model.sum(x[s] for s in S))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "for t in T:\n", - " model.add_constraint(model.sum(w[s][t] * x[s] for s in S) >= n[t])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Nurse_Scheduling.lp'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"Nurse_Scheduling.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 102.000000 after 0.02 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 2 rows and 0 columns.\n", - "Reduced MIP has 4 rows, 5 columns, and 8 nonzeros.\n", - "Reduced MIP has 0 binaries, 5 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.01 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "Reduced MIP has 4 rows, 5 columns, and 8 nonzeros.\n", - "Reduced MIP has 0 binaries, 5 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.02 sec. (0.01 ticks)\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 16 threads.\n", - "Root relaxation solution time = 0.00 sec. (0.00 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 102.0000 27.0000 73.53%\n", - "* 0 0 integral 0 61.0000 61.0000 3 0.00%\n", - "Elapsed time = 0.06 sec. (0.02 ticks, tree = 0.00 MB, solutions = 1)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.08 sec. (0.02 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.08 sec. (0.02 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=61,values={x_0:10,x_1:14,x_2:14,x_.." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Day 1, will have 10.0\n", - "Day 2, will have 24.0\n", - "Day 3, will have 28.0\n", - "Day 4, will have 14.0\n", - "Day 5, will have 23.0\n", - "Day 6, will have 23.0\n" - ] - } - ], - "source": [ - "obj = model.objective_value\n", - "assignment = [x[s].solution_value for s in S]\n", - "\n", - "for t in T:\n", - " print(f\"Day {t + 1}, will have {sum(w[s][t] * assignment[s] for s in S)}\")\n", - " assert sum(w[s][t] * assignment[s] for s in S) >= n[t]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/ILP/Television_plan.ipynb b/Madi/ILP/Television_plan.ipynb deleted file mode 100644 index c5ae618..0000000 --- a/Madi/ILP/Television_plan.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Television Show Planning**\n", - "John White is the program scheduling manager for the \n", - "television channel CCFO. John would like to plan the schedule of television shows for \n", - "next Wednesday evening.\n", - "The table below lists nine shows under consideration. John must select exactly \n", - "five of these shows for the period from 8:00 p.m. to 10:30 p.m. next Wednesday \n", - "evening. For each television show, the estimated advertising revenue (in $ millions) \n", - "is provided. Furthermore, each show has been categorized into one or more of the \n", - "categories “Public Interest,” “Violent,” “Comedy,” and “Drama.” In the following \n", - "table, a 1 indicates that the show is in the corresponding category and a 0 indicates \n", - "it is not\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ShowRevenue ($ Millions)Public InterestViolentComedyDrama
Sam's Place$60011
Texas Oil$100101
Cincinnati Law$91001
Jarred$40101
Bob & Mary$50010
Chainsaw$20100
Loving Life$61001
Islanders$70010
Urban Sprawl$81000
\n", - "\n", - "John would like to determine a revenue-maximizing schedule of television shows \n", - "for next Wednesday evening. However, he must be mindful of the following \n", - "considerations:\n", - "\n", - "• The schedule must include at least as many shows that are categorized as public \n", - "interest as shows that are categorized as violent.\n", - "\n", - "• If John schedules “Loving Life,” then he must also schedule either “Jarred” or \n", - "“Cincinnati Law” (or both).\n", - "\n", - "• John cannot schedule both “Loving Life” and “Urban Sprawl.”\n", - "\n", - "• If John schedules more than one show in the “Violent” category, he will lose an \n", - "estimated $4 million in advertising revenues from family-oriented sponsors.\n", - "\n", - "## **Sets**\n", - "$\\mathcal{I}$: Set of shows.\n", - "\n", - "## **Indecies**\n", - "$i \\in \\mathcal{I}$: Index of arbitrary show.\n", - "\n", - "## **Data**\n", - "$r_{i}, i \\in \\mathcal{I}$: Revenue gain for each show.\n", - "\n", - "$p_{i}, i \\in \\mathcal{I}$: Binary value indicating whether show $t$ was in public interest category.\n", - "\n", - "$v_{i}, i \\in \\mathcal{I}$: Binary value indicating whether show $t$ was in violent category.\n", - "\n", - "$c_{i}, i \\in \\mathcal{I}$: Binary value indicating whether show $t$ was in comedy category.\n", - "\n", - "$d_{i}, i \\in \\mathcal{I}$: Binary value indicating whether show $t$ was in drama category.\n", - "\n", - "$a$: Violent penalty.\n", - "\n", - "$n$: number of shows.\n", - "\n", - "## **Decision Vars**\n", - "$x_{i}, i \\in \\mathcal{I}$ Binary variable indicating whether to schedule show $i$ or not.\n", - "\n", - "$y$ Binary variable indicating whether the violent penalty applies or not.\n", - "\n", - "## **Formulation**\n", - "**Objective function**\n", - "\\begin{align*}\n", - "\\mathrm{Max}\\sum_{i \\in \\mathcal{I}}x_{i}r_{i} - ya\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\\begin{gather}\n", - "\\sum_{i \\in \\mathcal{I}} x_{i}p_{i} = \\sum_{i \\in \\mathcal{I}} x_{i}v_{i}\\\\\n", - "x_{7} \\le x_{3} + x_{4}\\\\\n", - "x_{7} \\ge x_{3}\\\\\n", - "x_{7} \\ge x_{4}\\\\\n", - "x_{7} = 1 - x_{9}\\\\\n", - "yn \\ge \\sum_{i \\in \\mathcal{I}} x_{i}v_{i} \\\\\n", - "x_{i} \\in \\{0, 1\\}, \\forall i \\in \\mathcal{I} \\\\\n", - "y \\in \\{0, 1\\}\n", - "\\end{gather}\n", - "\n", - "Helpfull link https://cs.stackexchange.com/questions/12102/express-boolean-logic-operations-in-zero-one-integer-linear-programming-ilp\n", - "\n", - "I think this can be generalized further, but its too much work, also not entirly sure about if statements." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name = \"Television Schedule\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "I = list(range(9))\n", - "\n", - "r = [6, 10, 9, 4, 5, 2, 6, 7, 8]\n", - "p = [0, 0, 1, 0, 0, 0, 1, 0, 1]\n", - "v = [0, 1, 0, 1, 0, 1, 0, 0, 0]\n", - "c = [1, 0, 0, 0, 1, 0, 0, 1, 0]\n", - "d = [1, 1, 1, 1, 0, 0, 1, 0, 0]\n", - "\n", - "a = 4\n", - "n = len(I)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "x = model.binary_var_list(I, name = \"x\")\n", - "y = model.binary_var(name = \"y\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model.maximize(model.sum(r[i] * x[i] for i in I) - a * y)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "docplex.mp.LinearConstraint[](9y,GE,x_1+x_3+x_5)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.add_constraint(model.sum(x[i] * p[i] for i in I) == model.sum(v[i] * x[i] for i in I))\n", - "model.add_constraint(x[6] <= x[2] + x[3])\n", - "model.add_constraint(x[6] >= x[2])\n", - "model.add_constraint(x[6] >= x[3])\n", - "model.add_constraint(x[6] == 1 - x[8])\n", - "model.add_constraint(y * n >= model.sum(v[i] * x[i] for i in I))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Television.lp'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"Television.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 2 times.\n", - "MIP Presolve eliminated 0 rows and 3 columns.\n", - "MIP Presolve modified 1 coefficients.\n", - "Aggregator did 1 substitutions.\n", - "Reduced MIP has 5 rows, 6 columns, and 15 nonzeros.\n", - "Reduced MIP has 6 binaries, 0 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.00 sec. (0.02 ticks)\n", - "Found incumbent of value 43.000000 after 0.02 sec. (0.02 ticks)\n", - "Probing fixed 1 vars, tightened 0 bounds.\n", - "Probing time = 0.00 sec. (0.00 ticks)\n", - "Cover probing fixed 0 vars, tightened 6 bounds.\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 5 rows and 6 columns.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.02 sec. (0.00 ticks)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.03 sec. (0.03 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.03 sec. (0.03 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=43,values={x_0:1,x_1:1,x_2:1,x_3:1.." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "43.0\n", - "[1.0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 0]\n", - "1.0\n" - ] - } - ], - "source": [ - "obj = model.objective_value\n", - "assignment = [x[i].solution_value for i in I]\n", - "assigny = y.solution_value\n", - "\n", - "\n", - "print(obj)\n", - "print(assignment)\n", - "print(assigny)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/LP/BankFunds.ipynb b/Madi/LP/BankFunds.ipynb deleted file mode 100644 index dcfc684..0000000 --- a/Madi/LP/BankFunds.ipynb +++ /dev/null @@ -1,325 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Bank Loan Funds Allocation**\n", - "\n", - "Adirondack Savings Bank (ASB) has $1 million in new funds that must be allocated to home loans, personal loans, and automobile loans. The annual rates of return for the three types of loans are 7% for home loans, 12% for personal loans, and 9% for automobile loans. The bank’s planning committee has decided that at least 40% of the new funds must be allocated to home loans. In addition, the planning committee has specified that the amount allocated to personal loans cannot exceed 60% of the amount allocated to automobile loans.\n", - "\n", - "a. Formulate a linear programming model that can be used to determine the amount of funds ASB should allocate to each type of loan to maximize the total annual return for the new funds.\n", - "\n", - "b. How much should be allocated to each type of loan? What is the total annual return? What is the annual percentage return?\n", - "\n", - "c. If the interest rate on home loans increases to 9%, would the amount allocated to each type of loan change? Explain.\n", - "\n", - "d. Suppose the total amount of new funds available is increased by $10,000. What effect would this have on the total annual return? Explain.\n", - "\n", - "e. Assume that ASB has the original $1 million in new funds available and that the planning committee has agreed to relax the requirement that at least 40% of the new funds must be allocated to home loans by 1%. How much would the annual return change? How much would the annual percentage return change?\n", - "\n", - "\n", - "## **Sets**\n", - "\n", - "$\\mathcal{R}$: Set of loan types.\n", - "\n", - "## **Indecies**\n", - "$r$: Index for element in $\\mathcal{R}$\n", - "\n", - "## **Data**\n", - "$l_{r}, r \\in \\mathcal{R}$ Return percentages for each loan type.\n", - "\n", - "$f$, Total availble funds/\n", - "\n", - "\n", - "## **Decision Variables**\n", - "$x_{r}, r \\in \\mathcal{R}$ Amount to allocate to each loan type.\n", - "\n", - "## **Formulation**\n", - "**Objective Function**\n", - "\\begin{align*}\n", - "\\mathrm{Max} \\sum_{r \\in \\mathcal{R}} x_{r} l_{r}\n", - "\\end{align*}\n", - "\n", - "**S.T.**\n", - "\\begin{gather}\n", - "\\sum_{r \\in \\mathcal{R}} x_{r} = f\\\\\n", - "x_{1} \\ge 0.4f\\\\\n", - "x_2 \\le 0.6x_{3}\\\\\n", - "x_{r} \\ge 0, \\forall r \\in \\mathcal{R}\n", - "\\end{gather}\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "model = Model(name = \"Bank_Funds\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "returns = [0.07, 0.12, 0.09]\n", - "f = 1000000\n", - "R = [0, 1, 2]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "x = model.continuous_var_list(len(returns), name = \"x\", lb = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model.maximize(model.sum(returns[r] * x[r] for r in R))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "model.add_constraint(model.sum(x[r] for r in R) <= f)\n", - "model.add_constraint(x[0] >= 0.4 * f)\n", - "model.add_constraint(x[1] <= 0.6 * x[2])\n", - "for r in R:\n", - " model.add_constraint(x[r] >= 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Bank_Funds.lp'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.export_as_lp(\"Bank_Funds.lp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Tried aggregator 1 time.\n", - "LP Presolve eliminated 5 rows and 2 columns.\n", - "Aggregator did 1 substitutions.\n", - "All rows and columns eliminated.\n", - "Presolve time = 0.02 sec. (0.00 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=88750,values={x_0:400000,x_1:22500.." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "88750.0\n" - ] - }, - { - "data": { - "text/plain": [ - "[400000.0, 225000.0, 375000.0]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obj = model.objective_value\n", - "assignment = [x[r].solution_value for r in R]\n", - "\n", - "\n", - "print(obj)\n", - "assignment" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_0+x_1+x_2 <= 1000000 0.10124999999999999\n", - "x_0 >= 400000.0 -0.031249999999999986\n", - "x_1 <= 0.600x_2 0.01875\n", - "x_0 >= 0 0\n", - "x_1 >= 0 0\n", - "x_2 >= 0 0\n" - ] - } - ], - "source": [ - "for ct in model.iter_constraints():\n", - " print(ct, ct.dual_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "cpx = model.get_engine().get_cplex()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(400000.0, 1e+20),\n", - " (0.0, 1000000.0),\n", - " (-360000.0, 600000.0),\n", - " (-1e+20, 400000.0),\n", - " (-1e+20, 225000.0),\n", - " (-1e+20, 375000.0)]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cpx.solution.sensitivity.objective()\n", - "cpx.solution.sensitivity.rhs()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_0+x_1+x_2 <= 1000000\n", - "0.10124999999999999\n", - "x_0 >= 400000.0\n", - "-0.031249999999999986\n", - "x_1 <= 0.600x_2\n", - "0.01875\n", - "x_0 >= 0\n", - "0\n", - "x_1 >= 0\n", - "0\n", - "x_2 >= 0\n", - "0\n" - ] - } - ], - "source": [ - "for ct in model.iter_constraints():\n", - " print(ct)\n", - " print(ct.dual_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_0\n", - "0\n", - "x_1\n", - "0\n", - "x_2\n", - "0\n" - ] - } - ], - "source": [ - "for ct in model.iter_variables():\n", - " print(ct)\n", - " print(ct.reduced_cost)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/Problems_for_fun/knapsack.ipynb b/Madi/Problems_for_fun/knapsack.ipynb deleted file mode 100644 index d7170d8..0000000 --- a/Madi/Problems_for_fun/knapsack.ipynb +++ /dev/null @@ -1,168 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# i was curious how fast it can solve knapsack problem\n", - "from docplex.mp.model import Model\n", - "model = Model(name = 'knapsack')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from random import randint\n", - "n = 100000\n", - "nums = [randint(100000, 100000000) for _ in range(n)]\n", - "weights = [randint(1, 100) for _ in range(n)]\n", - "cap = 300" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "x = model.binary_var_list(n, name = 'x')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "model.maximize(model.sum(x[i] * nums[i] for i in range(n)))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - 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- ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.add_constraint(model.sum(x[i] * weights[i] for i in range(n)) <= cap)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 0.000000 after 0.01 sec. (2.50 ticks)\n", - "Tried aggregator 1 time.\n", - "MIP Presolve eliminated 18 rows and 18 columns.\n", - "MIP Presolve added 4360 rows and 4360 columns.\n", - "Reduced MIP has 4343 rows, 104342 columns, and 113278 nonzeros.\n", - "Reduced MIP has 100000 binaries, 4342 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.31 sec. (62.45 ticks)\n", - "Tried aggregator 1 time.\n", - "Detecting symmetries...\n", - "Reduced MIP has 4343 rows, 104342 columns, and 113278 nonzeros.\n", - "Reduced MIP has 100000 binaries, 4342 generals, 0 SOSs, and 0 indicators.\n", - "Presolve time = 0.47 sec. (83.87 ticks)\n", - "Probing time = 0.14 sec. (2.95 ticks)\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 16 threads.\n", - "Root relaxation solution time = 19.22 sec. (183.77 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 0.0000 5.01731e+12 --- \n", - "* 0+ 0 5.13816e+09 5.01731e+12 --- \n", - "* 0 0 integral 0 2.59994e+10 2.59994e+10 301 0.00%\n", - "Elapsed time = 20.56 sec. (444.86 ticks, tree = 0.00 MB, solutions = 3)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 20.59 sec. (450.30 ticks)\n", - "Parallel b&c, 16 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 20.59 sec. 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n" - ] - } - ], - "source": [ - "obj = model.objective_value\n", - "assignment = [x[i].solution_value for i in range(n)]\n", - "print(obj, assignment)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Madi/mid.ipynb b/Madi/mid.ipynb deleted file mode 100644 index 50c7dc0..0000000 --- a/Madi/mid.ipynb +++ /dev/null @@ -1,142 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a4c714ba-b84a-431a-866f-784ad95f5ab4", - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c638f2fb-72e0-4c6f-878b-8338d983951d", - "metadata": {}, - "outputs": [], - "source": [ - "model = Model()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "89221321", - "metadata": {}, - "outputs": [], - "source": [ - "R1 = list(range(12)) # num of regions\n", - "R2 = list(range(12)) # decision vars\n", - "cover = [\n", - " [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n", - " [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n", - " [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n", - " [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n", - " [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n", - " [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "240cfb29-f1df-4d5f-84d8-db59023b410e", - "metadata": {}, - "outputs": [], - "source": [ - "x = model.binary_var_list(R2, name = \"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a0b2e3c5-8c88-4674-bb96-d2e00c618108", - "metadata": {}, - "outputs": [], - "source": [ - "model.minimize(model.sum(x[r] for r in R2))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "66b4a1de", - "metadata": {}, - "outputs": [], - "source": [ - "for r1 in R1:\n", - " model.add_constraint(model.sum(cover[r1][r2] * x[r2] for r2 in R2) >= 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2e55e1bc-8b49-48aa-b13f-d4d2326b8509", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=3,values={x_2:1,x_6:1,x_10:1})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5a61a107-0178-47e7-9d23-770dc9506d9f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0, 0, 1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0, 0]\n" - ] - } - ], - "source": [ - "model.objective_value\n", - "assignments = [x[r].solution_value for r in R2]\n", - "print(assignments)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Non Linear Optimization Models/ChairManufacturing_Problem.ipynb b/Non Linear Optimization Models/ChairManufacturing_Problem.ipynb deleted file mode 100644 index 2d5e7e2..0000000 --- a/Non Linear Optimization Models/ChairManufacturing_Problem.ipynb +++ /dev/null @@ -1,132 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Andalus Furniture Company has two manufacturing plants,\n", - "one at Aynor and another at Spartanburg. The cost in dollars of producing a kitchen\n", - "chair at each of the two plants is given here. The cost of producing Q1 chairs at Aynor is\n", - "\n", - "$75*Q_1 + 5Q_1^2 + 100$\n", - "\n", - "and the cost of producing Q2 kitchen chairs at Spartanburg is\n", - "\n", - "$25Q_2 + 2.5Q_2^2 + 150$\n", - "\n", - "Andalus needs to manufacture a total of 40 kitchen chairs to meet an order just\n", - "received. How many chairs should be made at Aynor, and how many should be made at\n", - "Spartanburg in order to minimize total production cost?" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 11250.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Reduced MIQP has 1 rows, 2 columns, and 2 nonzeros.\n", - "Reduced MIQP has 0 binaries, 2 generals, 0 SOSs, and 0 indicators.\n", - "Reduced MIQP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.01 sec. (0.00 ticks)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tried aggregator 1 time.\n", - "Reduced MIQP has 1 rows, 2 columns, and 2 nonzeros.\n", - "Reduced MIQP has 0 binaries, 2 generals, 0 SOSs, and 0 indicators.\n", - "Reduced MIQP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.01 sec. (0.00 ticks)\n", - "Classifier predicts products in MIQP should be linearized.\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 4 threads.\n", - "Root relaxation solution time = 0.01 sec. (0.01 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 11250.0000 250.0000 97.78%\n", - "* 0 0 integral 0 4500.0000 4500.0000 15 0.00%\n", - "Elapsed time = 0.05 sec. (0.02 ticks, tree = 0.00 MB, solutions = 2)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.05 sec. (0.02 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.05 sec. (0.02 ticks)\n" - ] - } - ], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "Q1 = m.integer_var(name='Chairs at Anyor')\n", - "Q2 = m.integer_var(name='Chairs at Spartanburg')\n", - "\n", - "m.add_constraint(Q1+Q2 >= 40)\n", - "\n", - "m.minimize(75*Q1 + 25*Q2 + 5*Q1**2 + 100 + 2.5*Q2**2 + 150) \n", - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model2\n", - "objective: 4500\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Chairs at Anyor=10\n", - "Chairs at Spartanburg=30\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/EconomicOrder_Problem.ipynb b/Non Linear Optimization Models/EconomicOrder_Problem.ipynb deleted file mode 100644 index 982ce2d..0000000 --- a/Non Linear Optimization Models/EconomicOrder_Problem.ipynb +++ /dev/null @@ -1,150 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The economic order quantity (EOQ) model is a clas-\n", - "sical model used for controlling inventory and satisfying demand. Costs included in\n", - "the model are holding cost per unit, ordering cost, and the cost of goods ordered. The\n", - "assumptions for that model are that only a single item is considered, that the entire\n", - "quantity ordered arrives at one time, that the demand for the item is constant over time,\n", - "and that no shortages are allowed.\n", - "Suppose we relax the first assumption and allow for multiple items that are\n", - "­independent except for a restriction on the amount of space available to store the\n", - "­products. The following model describes this situation:\n", - "Let\n", - "\n", - "\n", - "$D_j$ = annual demand for item j \n", - "\n", - "$C_j$ = unit cost of item j \n", - "\n", - "$S_j$ = cost per order placed for item j\n", - "\n", - "$w_j$ = space required for item j\n", - "\n", - "$W$ = the maximum amount of space available for all goods\n", - "\n", - "$i$ = inventory carrying charge as a percentage of the cost per unit\n", - "\n", - "\n", - "The decision variables are Qj, the amount of item j to order. The model is\n", - "\n", - "\n", - "\\begin{align*} \\text{Minimize} \\quad & \\sum_{j=1}^{n} \\left( C_j D_j + \\frac{S_jD_j}{Q_j} + i C_j\\frac{Q_j}{2} \\right) \\ \\text{s.t.} \\\\ \\quad & \\sum_{j=1}^{n} w_j Q_j \\leq W \\ \\\\ & Q_j \\geq 0 \\quad j = 1,2,…,N \\end{align*}\n", - "\n", - "In the objective function, the first term is the annual cost of goods, the second is the\n", - "annual ordering cost (Dj /Qj is the number of orders), and the last term is the annual\n", - "inventory holding cost (Qi /2 is the average amount of inventory).\n", - "Construct and solve a nonlinear optimization model for the following data:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Item 1 Item 2 Item 3\n", - "#\n", - "# Annual Demand 2,000 2,000 1,000\n", - "# Item Cost ($) 100 50 80\n", - "# Order Cost ($) 150 135 125\n", - "# Space Required 50 25 40 \n", - "# (sq. feet)\n", - "#\n", - "# W = 5000\n", - "# i = 0.20\n", - "\n", - "data = {'Item': ['Item 1', 'Item 2', 'Item 3'],\n", - " 'Annual Demand': [2000, 2000, 1000],\n", - " 'Item Cost ($)': [100, 50, 80],\n", - " 'Order Cost ($)': [150, 135, 125],\n", - " 'Space Required (sq. feet)': [50, 25, 40]}\n", - "\n", - "W = 5000\n", - "i_const = 0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Cplex isn't cplexing here, idk

" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "Argument 'exprs' should be a float expression", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 23\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mItem\u001b[39m\u001b[38;5;124m'\u001b[39m])):\n\u001b[1;32m 18\u001b[0m summation\u001b[38;5;241m.\u001b[39mappend(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mItem Cost ($)\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;241m*\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnnual Demand\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;241m+\u001b[39m \\\n\u001b[1;32m 19\u001b[0m (data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOrder Cost ($)\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;241m*\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnnual Demand\u001b[39m\u001b[38;5;124m'\u001b[39m][i])\u001b[38;5;241m/\u001b[39mQ_floated[i] \u001b[38;5;241m+\u001b[39m \\\n\u001b[1;32m 20\u001b[0m i_const \u001b[38;5;241m*\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mItem Cost ($)\u001b[39m\u001b[38;5;124m'\u001b[39m][i] \u001b[38;5;241m*\u001b[39m Q_floated[i]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 23\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43msummation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m m\u001b[38;5;241m.\u001b[39mexport_as_cpo\n\u001b[1;32m 27\u001b[0m solution \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39msolve(log_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/model.py:558\u001b[0m, in \u001b[0;36mCpoModel.minimize\u001b[0;34m(self, expr)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" Add an objective expression to minimize.\u001b[39;00m\n\u001b[1;32m 549\u001b[0m \n\u001b[1;32m 550\u001b[0m \u001b[38;5;124;03mDEPRECATED: use *add(minimize())* instead.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[38;5;124;03m Minimization expression that has been added\u001b[39;00m\n\u001b[1;32m 556\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 557\u001b[0m \u001b[38;5;66;03m# Add new minimization expression\u001b[39;00m\n\u001b[0;32m--> 558\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mminimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd(res)\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/modeler.py:1961\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(expr)\u001b[0m\n\u001b[1;32m 1945\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mminimize\u001b[39m(expr):\n\u001b[1;32m 1946\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\" This function asks CP Optimizer to seek to minimize the value of an expressions.\u001b[39;00m\n\u001b[1;32m 1947\u001b[0m \n\u001b[1;32m 1948\u001b[0m \u001b[38;5;124;03m The function *minimize* specifies to CP Optimizer a floating-point expression\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1959\u001b[0m \u001b[38;5;124;03m An objective expression\u001b[39;00m\n\u001b[1;32m 1960\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1961\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m CpoFunctionCall(Oper_minimize, Type_Objective, (\u001b[43m_convert_arg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexprs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mType_FloatExpr\u001b[49m\u001b[43m)\u001b[49m, ))\n", - "File \u001b[0;32m~/GithubRepos/BI/venv/lib/python3.10/site-packages/docplex/cp/modeler.py:334\u001b[0m, in \u001b[0;36m_convert_arg\u001b[0;34m(val, name, type, errmsg)\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" Convert a Python value in CPO and check its value\u001b[39;00m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m 328\u001b[0m \u001b[38;5;124;03m val: Value to convert\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[38;5;124;03m errmsg: Optional error message\u001b[39;00m\n\u001b[1;32m 332\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 333\u001b[0m val \u001b[38;5;241m=\u001b[39m build_cpo_expr(val)\n\u001b[0;32m--> 334\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m val\u001b[38;5;241m.\u001b[39mis_kind_of(\u001b[38;5;28mtype\u001b[39m), errmsg \u001b[38;5;28;01mif\u001b[39;00m errmsg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArgument \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m should be a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(name, \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m.\u001b[39mget_public_name())\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m val\n", - "\u001b[0;31mAssertionError\u001b[0m: Argument 'exprs' should be a float expression" - ] - } - ], - "source": [ - "from docplex.cp.model import CpoModel as Model\n", - "\n", - "m = Model() \n", - "\n", - "Q = m.integer_var_list(len(data['Item']))\n", - "Q_floated = [i for i in Q]\n", - "\n", - "for i in range(len(data['Item'])):\n", - " m.add_constraint(Q_floated[i] >= 0)\n", - "\n", - "items = sum(Q_floated[i] * data['Space Required (sq. feet)'][i] for i in range(len(data['Item'])))\n", - "\n", - "\n", - "m.add_constraint(items <= W)\n", - "\n", - "summation = []\n", - "for i in range(len(data['Item'])):\n", - " summation.append(data['Item Cost ($)'][i] * data['Annual Demand'][i] + \\\n", - " (data['Order Cost ($)'][i] * data['Annual Demand'][i])/Q_floated[i] + \\\n", - " i_const * data['Item Cost ($)'][i] * Q_floated[i]/2)\n", - "\n", - "\n", - "m.minimize(summation)\n", - "\n", - "m.export_as_cpo\n", - "\n", - "solution = m.solve(log_output=True)\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/EstimatingEconomicOutput_Problem.ipynb b/Non Linear Optimization Models/EstimatingEconomicOutput_Problem.ipynb deleted file mode 100644 index d12a922..0000000 --- a/Non Linear Optimization Models/EstimatingEconomicOutput_Problem.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Cobb-Douglas production function is a classic model\n", - "from economics used to model output as a function of capital and labor. It has the form:\n", - "\n", - "$\\mathcal{f}(L, C) = c_0 L^{c_1} C^{c_2}$\n", - "\n", - "where c0, c1, and c2 are constants.
\n", - "The variable L represents the units of input of labor,
\n", - "and the variable C represents the units of input of capital.
\n", - "\n", - "\n", - "a. In this example, assume c0 = 5, c1 = 0.25, and c2 = 0.75. Assume each unit of labor\n", - "costs $25 and each unit of capital costs $75. With $75,000 available in the budget,\n", - "develop an optimization model to determine how the budgeted amount should be\n", - "allocated between capital and labor in order to maximize output.\n", - "\n", - "b. Find the optimal solution to the model you formulated in part (a). (Hint: When\n", - "using Excel Solver, use the Multistart option with bounds 0 <= L <= 3,000 and\n", - "0 <= C <= 1,000.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Not sure about this one because mp model doesn't solve this

" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ! --------------------------------------------------- CP Optimizer 22.1.1.0 --\n", - " ! Maximization problem - 2 variables, 1 constraint\n", - " ! Initial process time : 0.01s (0.01s extraction + 0.00s propagation)\n", - " ! . Log search space : 34.8 (before), 34.8 (after)\n", - " ! . Memory usage : 267.0 kB (before), 267.0 kB (after)\n", - " ! Using parallel search with 4 workers.\n", - " ! ----------------------------------------------------------------------------\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 0 2 -\n", - " + New bound is 6.580370e+07\n", - " 0 2 1 F -\n", - " + New bound is 3.750228e+07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * 0 2 0.08s 1 (gap > 10000%)\n", - " * 3.053527e+07 6 0.08s 1 (gap is 22.82%)\n", - " * 3.429332e+07 17 0.08s 1 (gap is 9.36%)\n", - " * 3.429696e+07 20 0.08s 1 (gap is 9.35%)\n", - " * 3.430058e+07 21 0.08s 1 (gap is 9.33%)\n", - " * 3.430406e+07 23 0.08s 1 (gap is 9.32%)\n", - " * 3.655943e+07 25 0.08s 1 (gap is 2.58%)\n", - " * 3.678016e+07 27 0.08s 1 (gap is 1.96%)\n", - " * 3.695594e+07 29 0.08s 1 (gap is 1.48%)\n", - " * 3.695978e+07 30 0.08s 1 (gap is 1.47%)\n", - " * 3.696352e+07 31 0.08s 1 (gap is 1.46%)\n", - " * 3.696730e+07 32 0.08s 1 (gap is 1.45%)\n", - " * 3.711001e+07 34 0.08s 1 (gap is 1.06%)\n", - " * 3.747341e+07 36 0.08s 1 (gap is 0.08%)\n", - " * 3.748005e+07 38 0.08s 1 (gap is 0.06%)\n", - " * 3.748380e+07 39 0.08s 1 (gap is 0.05%)\n", - " * 3.748756e+07 40 0.08s 1 (gap is 0.04%)\n", - " * 3.749132e+07 41 0.08s 1 (gap is 0.03%)\n", - " ! Time = 0.08s, Average fail depth = 3, Memory usage = 1.2 MB\n", - " ! Current bound is 3.750228e+07 (gap is 0.03%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 3.749512e+07 43 0.08s 1 (gap is 0.02%)\n", - " * 3.749918e+07 45 0.08s 1 (gap is 0.01%)\n", - " ! ----------------------------------------------------------------------------\n", - " ! Search completed, 20 solutions found.\n", - " ! Best objective : 3.749918e+07 (optimal - effective tol. is 3749.92)\n", - " ! Best bound : 3.750228e+07\n", - " ! ----------------------------------------------------------------------------\n", - " ! Number of branches : 649\n", - " ! Number of fails : 349\n", - " ! Total memory usage : 3.0 MB (2.9 MB CP Optimizer + 0.0 MB Concert)\n", - " ! Time spent in solve : 0.08s (0.07s engine + 0.01s extraction)\n", - " ! Search speed (br. / s) : 10816.7\n", - " ! ----------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "from docplex.cp.model import CpoModel as Model\n", - "\n", - "m = Model() \n", - "\n", - "c0 = 5\n", - "c1 = 0.25\n", - "c2 = 0.75\n", - "\n", - "L = m.integer_var(name='Labor')\n", - "C = m.integer_var(name='Capital')\n", - "\n", - "L_float = L / 100\n", - "C_float = C / 100\n", - "\n", - "m.add_constraint(25*L_float + 75*C_float <= 75000)\n", - "\n", - "m.maximize(c0 * 100*L_float**c1 * 100*C_float**c2)\n", - "\n", - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "742.16 Labor\n", - "752.61 Capital\n" - ] - } - ], - "source": [ - "print(solution[L]/100,\" Labor\")\n", - "print(solution[C]/100,\" Capital\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "74999.75" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution[L]/100 * 25 + solution[C]/100 * 75" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3749.9189289224228\n" - ] - } - ], - "source": [ - "print(solution.get_objective_value())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/ExponentialSmoothin_Problem.ipynb b/Non Linear Optimization Models/ExponentialSmoothin_Problem.ipynb deleted file mode 100644 index d1cf045..0000000 --- a/Non Linear Optimization Models/ExponentialSmoothin_Problem.ipynb +++ /dev/null @@ -1,245 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exponential Smoothing. Many forecasting models use parameters that are estimated\n", - "using nonlinear optimization. A good example is the Bass model introduced in this chapter.\n", - "Another example is the exponential smoothing forecasting model which is a common fore-\n", - "casting model used in practice. For instance, the basic exponential smoothing model for fore-\n", - "casting sales is\n", - "\n", - "$\\hat{y}_{t+1} = ay_t + (1 - a) \\hat{y}_t$\n", - "\n", - "where\n", - "\n", - "$\\hat{y}_{t+1}$ = forecast of sales for period t + 1\n", - "\n", - "$y_t$ = actual sales for period t\n", - "\n", - "$\\hat{y}_t$ = forecast of sales for period t\n", - "\n", - "$\\alpha{}$ = smoothing constant, 0 <= a <= 1\n", - "\n", - "This model is used recursively; the forecast for time period t + 1 is based on the\n", - "forecast for period t, $ŷ_t$ , the observed value of sales in period t, $y_t$ , and the smoothing\n", - "parameter a . The use of this model to forecast sales for 12 months is illustrated in the\n", - "following table with the smoothing constant $\\alpha$ = 0.3. The forecast errors, $y_t - \\hat{y}_t$ , are\n", - "calculated in the fourth column. The value of a is often chosen by minimizing the sum\n", - "of squared forecast errors. The last column of the table shows the square of the forecast\n", - "error and the sum of squared forecast errors.\n", - "\n", - "The file ExpSmooth contains the observed data shown here. Construct this table\n", - "using the formula above. Note that we set the forecast in period 1 to the observed\n", - "in period 1 to get started ( $\\hat{y}_1$ = $y_1$ = 17 ), then the formula above for $\\hat{y}_{t+1}$ is used\n", - "starting in period 2. Make sure to have a single cell corresponding to a in your\n", - "spreadsheet model. After confirming the values in the table below with $\\alpha$ = 0.3, try\n", - "different values of a to see if you can get a smaller sum of squared forecast errors." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Week | Observed Value | Forecast Value | Error | Squared Error\n", - "# (t) | (y_t) | (y^_t) | (yt - y^t) | (yt - y^t)^2\n", - "# 1 | 17 | 17.00 | 0.00 | 0.00\n", - "# 2 | 21 | 17.00 | 4.00 | 16.00\n", - "# 3 | 19 | 18.20 | 0.80 | 0.64\n", - "# 4 | 23 | 18.44 | 4.56 | 20.79\n", - "# 5 | 18 | 19.81 | -1.8 | 13.27\n", - "# 6 | 16 | 19.27 | -3.2 | 710.66\n", - "# 7 | 20 | 18.29 | 1.71 | 2.94\n", - "# 8 | 18 | 18.80 | -0.80 | 0.64\n", - "# 9 | 22 | 18.56 | 3.44 | 11.83\n", - "# 10 | 20 | 19.59 | 0.41 | 0.17\n", - "# 11 | 15 | 19.71 | -4.71 | 22.23\n", - "# 12 | 22 | 18.30 | 3.70 | 13.69\n", - "# SUM | -- | -- | -- | 102.86\n", - "\n", - "data = {'week': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n", - " 'true': [17, 21, 19, 23, 18, 16, 20, 18, 22, 20, 15, 22],\n", - " 'predicted': [17.00, 17.00, 18.20, 18.44, 19.81, 19.27, 18.29, 18.80, 18.56, 19.59, 19.71, 18.30],\n", - " 'error': [0.00, 4.00, 0.80, 4.56, -1.8, -3.2, 1.71, -0.80, 3.44, 0.41, -4.71, 3.70],\n", - " 'error_squared': [0.00, 16.00, 0.64, 20.79, 13.27, 710.66, 2.94, 0.64, 11.83, 0.17, 22.23, 13.69]}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ! --------------------------------------------------- CP Optimizer 22.1.1.0 --\n", - " ! Minimization problem - 1 variable, 2 constraints\n", - " ! Initial process time : 0.00s (0.00s extraction + 0.00s propagation)\n", - " ! . Log search space : 10.0 (before), 10.0 (after)\n", - " ! . Memory usage : 299.7 kB (before), 299.7 kB (after)\n", - " ! Using parallel search with 4 workers.\n", - " ! ----------------------------------------------------------------------------\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 0 1 -\n", - " + New bound is 16\n", - " 0 1 1 -\n", - " + New bound is 35.16110\n", - " * 131 1 0.08s 1 (gap is 73.16%)\n", - " * 130.5318 3 0.08s 1 (gap is 73.06%)\n", - " * 114.6853 4 0.08s 1 (gap is 69.34%)\n", - " * 106.5805 13 0.08s 1 (gap is 67.01%)\n", - " * 106.4031 151 0.08s 1 (gap is 66.95%)\n", - " * 105.3095 2 0.08s 2 (gap is 66.61%)\n", - " * 102.0352 4 0.08s 2 (gap is 65.54%)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * 98.74611 16 0.08s 2 (gap is 64.39%)\n", - " * 98.59555 765 0.08s 3 (gap is 64.34%)\n", - " 98.59555 1000 1 3 F 11 = alpha\n", - " * 98.56407 18 0.08s 4 (gap is 64.33%)\n", - " 98.56407 2000 1 3 F 7 = alpha\n", - " 98.56407 3000 1 3 F -\n", - " 98.56407 1000 1 1 F 836 = alpha\n", - " * 98.55956 698 0.18s 2 (gap is 64.33%)\n", - " 98.55956 4000 1 3 F 659 = alpha\n", - " 98.55956 1000 1 2 850 != alpha\n", - " 98.55956 5000 1 3 F 193 = alpha\n", - " ! Time = 0.25s, Average fail depth = 8, Memory usage = 3.2 MB\n", - " ! Current bound is 35.16110 (gap is 64.33%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 98.55956 1000 1 4 300 != alpha\n", - " 98.55956 6000 1 3 F 320 = alpha\n", - " 98.55956 7000 1 3 -\n", - " 98.55956 8000 1 3 F 900 = alpha\n", - " 98.55956 9000 1 3 -\n", - " 98.55956 2000 1 1 682 != alpha\n", - " 98.55956 2000 1 2 507 != alpha\n", - " 98.55956 1706 1 4 -\n", - " + New bound is 98.55422 (gap is 0.01%)\n", - " ! ----------------------------------------------------------------------------\n", - " ! Search completed, 11 solutions found.\n", - " ! Best objective : 98.55956 (optimal - effective tol. is 0.00985596)\n", - " ! Best bound : 98.55422\n", - " ! ----------------------------------------------------------------------------\n", - " ! Number of branches : 15759\n", - " ! Number of fails : 8003\n", - " ! Total memory usage : 3.6 MB (3.6 MB CP Optimizer + 0.0 MB Concert)\n", - " ! Time spent in solve : 0.43s (0.43s engine + 0.00s extraction)\n", - " ! Search speed (br. / s) : 37521.4\n", - " ! ----------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "from docplex.cp.model import CpoModel as Model\n", - "\n", - "\n", - "m = Model()\n", - "\n", - "alpha = m.integer_var(name = 'alpha')\n", - "\n", - "m.add_constraint(alpha >= 0)\n", - "m.add_constraint(alpha <=1000)\n", - "\n", - "alpha_float = alpha/1000\n", - "\n", - "start_value = 17\n", - "\n", - "predicted = [17]\n", - "\n", - "for i in range(1, len(data['week'])):\n", - " predicted.append(alpha_float * data['true'][i-1] + (1 - alpha_float) * predicted[i-1])\n", - "\n", - "error = [(pred - tru)**2 for pred, tru in zip(predicted, data['true'])]\n", - "\n", - "m.minimize(m.sum(error))\n", - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "m.export_as_cpo('alpha.cpo')" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.173 alpha\n", - "98.55956125237986\n" - ] - } - ], - "source": [ - "print(solution[alpha]/1000,\" alpha\")\n", - "print(solution.get_objective_value())" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "98.57150185864782" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "other_pred = [17]\n", - "\n", - "thing_alpha = 0.18\n", - "\n", - "for i in range(1, len(data['week'])):\n", - " other_pred.append(thing_alpha * data['true'][i-1] + (1 - thing_alpha) * other_pred[i-1])\n", - "\n", - "error = [(pred - tru)**2 for pred, tru in zip(other_pred, data['true'])]\n", - "sum(error)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/ForcastingAdoption_Problem.ipynb b/Non Linear Optimization Models/ForcastingAdoption_Problem.ipynb deleted file mode 100644 index 324806b..0000000 --- a/Non Linear Optimization Models/ForcastingAdoption_Problem.ipynb +++ /dev/null @@ -1,40 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{align*}\n", - "\\mathrm{minimize} \\sum_{i=2}^{|T|}{((P+Q(C_{i-1}/M)) (M-C_{i-1}) - S_i)} + PM \\\\\n", - "\\\\\n", - "\\text{or} \\\\\n", - "\\\\\n", - "\\mathrm{minimize} \\sum_{i=2}^{|T|}{E_t^2} \\\\\n", - "\\text{st} \\\\\n", - "E_i = F_i - C_i \\forall i \\in X \\\\\n", - "F_i = \\text{In the slides} \\\\\n", - "\n", - "\\end{align*}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/MediaPlanning_Problem.ipynb b/Non Linear Optimization Models/MediaPlanning_Problem.ipynb deleted file mode 100644 index d2f6c91..0000000 --- a/Non Linear Optimization Models/MediaPlanning_Problem.ipynb +++ /dev/null @@ -1,156 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GreenLawns provides a lawn fertilizing and weed control service.\n", - "The company is adding a special aeration treatment as a low-cost extra service option\n", - "that it hopes will help attract new customers. Management is planning to promote this \n", - "new service in two media: radio and direct-mail advertising. A media budget of $3,000\n", - "is available for this promotional campaign. Based on past experience in promoting its\n", - "other services, GreenLawns has obtained the following estimate of the relationship\n", - "between sales and the amount spent on promotion in these two media:\n", - "\n", - "$S = -2R^2 - 10M^2 -8RM + 18R + 34M$\n", - "\n", - "where\n", - "\n", - "$S = \\text{total sales in thousands of dollars} \\\\\n", - "R = \\text{thousands of dollars spent on radio advertising} \\\\\n", - "M = \\text{thousands of dollars spent on direct-mail advertising}$\n", - "\n", - "GreenLawns would like to develop a promotional strategy that will lead to maximum\n", - "sales subject to the restriction provided by the media budget.\n", - "\n", - "Formulate an optimization problem that can be solved to maximize sales subject to\n", - "the media budget of spending no more than $3,000 on total advertising." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "R = m.continuous_var(name= 'Radio ads')\n", - "M = m.continuous_var(name= 'Mail ads')\n", - "\n", - "m.add_constraint(R+M <= 3)\n", - "\n", - "m.maximize(-2*(R**2) - 10*(M**2) - 8*R*M + 18*R + 34*M)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.01 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 2\n", - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - "Tried aggregator 1 time.\n", - "QP Presolve added 0 rows and 2 columns.\n", - "Reduced QP has 3 rows, 4 columns, and 7 nonzeros.\n", - "Reduced QP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.07 sec. (0.00 ticks)\n", - "Parallel mode: using up to 4 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 3\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.01 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 4\n", - " Rows in Factor = 3\n", - " Integer space required = 3\n", - " Total non-zeros in factor = 6\n", - " Total FP ops to factor = 14\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 5.1598616e+01 4.0138408e-01 7.30e+00 0.00e+00 3.05e+03\n", - " 1 2.6468801e+01 3.6432950e+02 8.88e-16 0.00e+00 9.95e-14\n", - " 2 2.8124631e+01 5.0442672e+01 8.88e-16 0.00e+00 5.68e-14\n", - " 3 3.2749451e+01 3.8079631e+01 4.00e-15 0.00e+00 9.99e-15\n", - " 4 3.6672627e+01 3.7797116e+01 1.33e-14 0.00e+00 1.12e-14\n", - " 5 3.6884712e+01 3.7253412e+01 0.00e+00 0.00e+00 2.28e-15\n", - " 6 3.6959582e+01 3.7080335e+01 1.33e-15 0.00e+00 6.03e-15\n", - " 7 3.6986608e+01 3.7026541e+01 0.00e+00 0.00e+00 3.15e-15\n", - " 8 3.6995563e+01 3.7008834e+01 8.88e-16 0.00e+00 2.05e-15\n", - " 9 3.6998525e+01 3.7002944e+01 0.00e+00 0.00e+00 2.67e-15\n", - " 10 3.6999509e+01 3.7000981e+01 2.22e-15 0.00e+00 3.67e-15\n", - " 11 3.6999836e+01 3.7000327e+01 0.00e+00 0.00e+00 5.07e-15\n", - " 12 3.6999945e+01 3.7000109e+01 8.88e-16 0.00e+00 3.46e-15\n", - " 13 3.6999982e+01 3.7000036e+01 2.66e-15 0.00e+00 8.48e-16\n", - " 14 3.6999994e+01 3.7000012e+01 4.00e-15 0.00e+00 3.12e-15\n", - " 15 3.6999998e+01 3.7000004e+01 8.88e-16 0.00e+00 7.49e-15\n", - " 16 3.6999999e+01 3.7000001e+01 2.22e-15 0.00e+00 6.14e-15\n", - " 17 3.7000000e+01 3.7000000e+01 1.78e-15 0.00e+00 1.47e-15\n", - " 18 3.7000000e+01 3.7000000e+01 3.11e-15 0.00e+00 4.42e-15\n", - "Barrier time = 0.16 sec. (0.02 ticks)\n", - "\n", - "Total time on 4 threads = 0.16 sec. (0.02 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model4\n", - "objective: 37\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Radio ads=2.500\n", - "Mail ads=0.500\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/PricingCameras_Problem.ipynb b/Non Linear Optimization Models/PricingCameras_Problem.ipynb deleted file mode 100644 index 7c534ce..0000000 --- a/Non Linear Optimization Models/PricingCameras_Problem.ipynb +++ /dev/null @@ -1,711 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pricing Cameras. Jim’s Camera shop sells two high-end cameras, the Sky\n", - "Eagle and Horizon. The demands for these two cameras are as follows:\n", - "DS = demand for the Sky Eagle, PS is the selling price of the Sky Eagle, DH is the\n", - "demand for the Horizon, and PH is the ­selling price of the Horizon.\n", - "\n", - "$\n", - "D_S = 222 - 0.60 P_S + 0.35 P_H \\\\\n", - "D_H = 270 + 0.10 P_S - 0.64 P_H\n", - "$\n", - "\n", - "The store wishes to determine the selling price that maximizes revenue for these two\n", - "products. Develop the revenue function for these two models, and find the prices that\n", - "maximize revenue." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ! --------------------------------------------------- CP Optimizer 22.1.1.0 --\n", - " ! Maximization problem - 2 variables, 4 constraints\n", - " ! Initial process time : 0.00s (0.00s extraction + 0.00s propagation)\n", - " ! . Log search space : 46.5 (before), 46.5 (after)\n", - " ! . Memory usage : 267.0 kB (before), 267.0 kB (after)\n", - " ! Using parallel search with 4 workers.\n", - " ! ----------------------------------------------------------------------------\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 0 2 -\n", - " + New bound is 4.500492e+13\n", - " 0 2 1 F -\n", - " + New bound is 1.000310e+13\n", - " * 0 2 0.04s 1 (gap > 10000%)\n", - " * 269.3599 14 0.04s 1 (gap > 10000%)\n", - " * 441.5999 54 0.04s 1 (gap > 10000%)\n", - " * 537.4399 40 0.04s 2 (gap > 10000%)\n", - " 537.4399 1000 1 3 30 != H\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * 660.5999 256 0.07s 1 (gap > 10000%)\n", - " * 804.2399 1034 0.07s 3 (gap > 10000%)\n", - " * 1069.759 1192 0.07s 3 (gap > 10000%)\n", - " 1069.759 2000 1 3 4131207 != H\n", - " * 1333.999 2080 0.09s 3 (gap > 10000%)\n", - " * 1596.959 2138 0.09s 3 (gap > 10000%)\n", - " * 1858.639 2474 0.09s 3 (gap > 10000%)\n", - " * 15656.35 2515 0.09s 3 (gap > 10000%)\n", - " * 15767.80 2798 0.09s 3 (gap > 10000%)\n", - " * 27184.15 2927 0.09s 3 (gap > 10000%)\n", - " 27184.15 3000 1 3 -\n", - " * 27403.99 3622 0.11s 3 (gap > 10000%)\n", - " * 28218.95 3708 0.11s 3 (gap > 10000%)\n", - " ! Time = 0.11s, Average fail depth = 9, Memory usage = 3.8 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 28453.03 3959 0.11s 3 (gap > 10000%)\n", - " 28453.03 4000 1 3 F 1 = H\n", - " 28453.03 1000 1 1 F 9999922 = S\n", - " 28453.03 1000 1 2 F 9999923 = H\n", - " * 28471.03 4330 0.15s 3 (gap > 10000%)\n", - " * 28474.15 4530 0.15s 3 (gap > 10000%)\n", - " 28474.15 1000 1 4 1964672 != S\n", - " 28474.15 5000 1 3 F -\n", - " * 28544.30 5027 0.16s 3 (gap > 10000%)\n", - " * 28554.83 5127 0.16s 3 (gap > 10000%)\n", - " 28554.83 6000 1 3 F -\n", - " * 28593.55 6378 0.19s 3 (gap > 10000%)\n", - " * 28601.65 6440 0.19s 3 (gap > 10000%)\n", - " * 28623.14 6702 0.19s 3 (gap > 10000%)\n", - " * 28786.03 6882 0.20s 3 (gap > 10000%)\n", - " 28786.03 7000 1 3 F 31 = H\n", - " * 28792.63 1917 0.22s 1 (gap > 10000%)\n", - " * 28792.90 7415 0.22s 3 (gap > 10000%)\n", - " * 28808.59 7763 0.22s 3 (gap > 10000%)\n", - " * 28861.43 7804 0.22s 3 (gap > 10000%)\n", - " ! Time = 0.22s, Average fail depth = 10, Memory usage = 4.8 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 28885.93 7874 0.22s 3 (gap > 10000%)\n", - " 28885.93 2000 1 1 181426 != H\n", - " 28885.93 8000 1 3 -\n", - " * 28909.15 8476 0.25s 3 (gap > 10000%)\n", - " * 29099.83 8639 0.25s 3 (gap > 10000%)\n", - " 29099.83 9000 1 3 -\n", - " 29099.83 2000 1 4 F 9999886 = H\n", - " 29099.83 2000 1 2 F 2804879 = H\n", - " * 29103.85 9249 0.30s 3 (gap > 10000%)\n", - " * 29140.60 9694 0.30s 3 (gap > 10000%)\n", - " 29140.60 10000 1 3 F 437562 = S\n", - " * 29416.39 10322 0.34s 3 (gap > 10000%)\n", - " * 29579.03 10603 0.34s 3 (gap > 10000%)\n", - " 29579.03 11000 1 3 F 2887420 = H\n", - " * 29585.23 11316 0.37s 3 (gap > 10000%)\n", - " * 29604.23 11642 0.37s 3 (gap > 10000%)\n", - " * 29621.95 11822 0.38s 3 (gap > 10000%)\n", - " 29621.95 12000 1 3 9999971 != S\n", - " 29621.95 3000 1 1 F 7788959 = S\n", - " * 29638.39 12410 0.40s 3 (gap > 10000%)\n", - " ! Time = 0.40s, Average fail depth = 10, Memory usage = 5.4 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 29638.39 3000 1 4 9039477 != H\n", - " 29638.39 3000 1 2 6873414 != S\n", - " * 29731.39 12926 0.43s 3 (gap > 10000%)\n", - " 29731.39 13000 1 3 9948498 != S\n", - " * 29746.90 13799 0.45s 3 (gap > 10000%)\n", - " 29746.90 14000 1 3 9847710 != H\n", - " * 30044.74 14107 0.45s 3 (gap > 10000%)\n", - " * 30047.83 14442 0.48s 3 (gap > 10000%)\n", - " 30047.83 15000 1 3 F 2023329 = S\n", - " * 30348.40 15096 0.48s 3 (gap > 10000%)\n", - " * 30554.59 15168 0.48s 3 (gap > 10000%)\n", - " * 33330.23 15361 0.48s 3 (gap > 10000%)\n", - " * 33536.59 15774 0.52s 3 (gap > 10000%)\n", - " 33536.59 16000 1 3 F 4 = H\n", - " * 33711.34 16106 0.52s 3 (gap > 10000%)\n", - " * 33987.25 16615 0.54s 3 (gap > 10000%)\n", - " 33987.25 17000 1 3 F 7202627 = S\n", - " * 34105.99 17174 0.54s 3 (gap > 10000%)\n", - " 34105.99 18000 1 3 F -\n", - " 34105.99 4000 1 1 1647012 != H\n", - " ! Time = 0.58s, Average fail depth = 10, Memory usage = 5.6 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 34141.15 18068 0.58s 3 (gap > 10000%)\n", - " 34141.15 4000 1 4 7467761 != S\n", - " 34141.15 4000 1 2 F 53526 = H\n", - " * 34268.99 18601 0.60s 3 (gap > 10000%)\n", - " 34268.99 19000 1 3 F -\n", - " * 34424.50 19164 0.62s 3 (gap > 10000%)\n", - " * 34542.23 19898 0.62s 3 (gap > 10000%)\n", - " * 34706.65 19981 0.64s 3 (gap > 10000%)\n", - " 34706.65 20000 1 3 -\n", - " * 34987.60 20238 0.64s 3 (gap > 10000%)\n", - " 34987.60 21000 1 3 F 9999969 = S\n", - " * 35022.83 21099 0.65s 3 (gap > 10000%)\n", - " * 35159.05 21563 0.68s 3 (gap > 10000%)\n", - " * 35303.03 21754 0.68s 3 (gap > 10000%)\n", - " * 35582.03 21905 0.68s 3 (gap > 10000%)\n", - " 35582.03 22000 1 3 -\n", - " 35582.03 5000 1 1 F 4 = H\n", - " * 35589.43 22975 0.72s 3 (gap > 10000%)\n", - " 35589.43 23000 1 3 -\n", - " 35589.43 5000 1 4 F 8399522 = S\n", - " ! Time = 0.72s, Average fail depth = 10, Memory usage = 5.9 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 35589.43 24000 1 3 F 20 = S\n", - " 35589.43 5000 1 2 224295 != S\n", - " * 35859.83 24151 0.75s 3 (gap > 10000%)\n", - " 35859.83 25000 1 3 F 5812269 = H\n", - " * 36099.40 25578 0.80s 3 (gap > 10000%)\n", - " * 36299.39 25669 0.80s 3 (gap > 10000%)\n", - " 36299.39 26000 1 3 F 2551831 = H\n", - " * 36584.09 26122 0.80s 3 (gap > 10000%)\n", - " * 36691.55 26438 0.84s 3 (gap > 10000%)\n", - " * 36969.80 26923 0.84s 3 (gap > 10000%)\n", - " 36969.80 27000 1 3 F 9999972 = H\n", - " 36969.80 6000 1 1 4427198 != H\n", - " * 37230.83 27918 0.86s 3 (gap > 10000%)\n", - " 37230.83 6000 1 4 5475857 != S\n", - " 37230.83 28000 1 3 4989675 != H\n", - " 37230.83 6000 1 2 F 8917040 = H\n", - " * 37296.73 28662 0.88s 3 (gap > 10000%)\n", - " * 37573.48 28703 0.88s 3 (gap > 10000%)\n", - " * 37659.68 28776 0.88s 3 (gap > 10000%)\n", - " 37659.68 29000 1 3 F 4 = H\n", - " ! Time = 0.91s, Average fail depth = 10, Memory usage = 6.5 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 64304.50 29505 0.93s 3 (gap > 10000%)\n", - " 64304.50 30000 1 3 -\n", - " 64304.50 31000 1 3 -\n", - " 64304.50 7000 1 1 F 5695668 = H\n", - " 64304.50 7000 1 4 F 5804084 = S\n", - " 64304.50 32000 1 3 F 5 = S\n", - " 64304.50 7000 1 2 3070162 != S\n", - " 64304.50 33000 1 3 F 26 = H\n", - " 64304.50 34000 1 3 -\n", - " * 66578.95 34599 1.10s 3 (gap > 10000%)\n", - " 66578.95 35000 1 3 9999982 != H\n", - " * 67407.13 35434 1.10s 3 (gap > 10000%)\n", - " * 67457.98 35659 1.13s 3 (gap > 10000%)\n", - " * 68049.40 35725 1.13s 3 (gap > 10000%)\n", - " 68049.40 36000 1 3 F -\n", - " 68049.40 37000 1 3 F 31 = H\n", - " 68049.40 8000 1 1 F 7142911 = H\n", - " 68049.40 8000 1 4 F 189 = H\n", - " 68049.40 38000 1 3 F -\n", - " 68049.40 8000 1 2 8448805 != H\n", - " ! Time = 1.19s, Average fail depth = 10, Memory usage = 7.0 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 68049.40 39000 1 3 F 3782982 = S\n", - " 68049.40 40000 1 3 F 28 = H\n", - " * 69211.33 40472 1.29s 3 (gap > 10000%)\n", - " 69211.33 41000 1 3 9999997 != H\n", - " 69211.33 9000 1 4 8893352 != S\n", - " 69211.33 42000 1 3 5580414 != S\n", - " 69211.33 9000 1 1 F 819111 = S\n", - " 69211.33 9000 1 2 F 4225075 = H\n", - " * 69232.48 42336 1.37s 3 (gap > 10000%)\n", - " 69232.48 43000 1 3 -\n", - " 69232.48 44000 1 3 F 4801308 = S\n", - " 69232.48 45000 1 3 F 9225299 = H\n", - " * 69249.68 45062 1.48s 3 (gap > 10000%)\n", - " 69249.68 46000 1 3 -\n", - " 69249.68 10000 1 1 F 9999237 = S\n", - " 69249.68 47000 1 3 F 29 = S\n", - " 69249.68 10000 1 4 F 2970516 = S\n", - " 69249.68 48000 1 3 F -\n", - " 69249.68 10000 1 2 F 6800475 = S\n", - " 69249.68 49000 1 3 -\n", - " ! Time = 1.58s, Average fail depth = 10, Memory usage = 8.2 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 69249.68 50000 1 3 F -\n", - " 69249.68 51000 1 3 F 26 = H\n", - " 69249.68 52000 1 3 -\n", - " 69249.68 11000 1 4 F 9999121 = H\n", - " 69249.68 11000 1 1 247 != S\n", - " * 69259.78 52308 1.65s 3 (gap > 10000%)\n", - " * 69317.53 11171 1.65s 1 (gap > 10000%)\n", - " * 69325.48 11223 1.67s 4 (gap > 10000%)\n", - " 69325.48 11000 1 2 F 165 = H\n", - " * 75531.95 52971 1.69s 3 (gap > 10000%)\n", - " 75531.95 53000 1 3 F -\n", - " 75531.95 54000 1 3 F 2293253 = S\n", - " 75531.95 12000 1 4 F 9999078 = H\n", - " 75531.95 55000 1 3 F 9999970 = S\n", - " 75531.95 56000 1 3 9999974 != S\n", - " 75531.95 12000 1 1 F 172 = H\n", - " 75531.95 57000 1 3 F 1 != S\n", - " 75531.95 12000 1 2 F 175 = H\n", - " 75531.95 58000 1 3 F 3 = S\n", - " 75531.95 13000 1 4 F 9999037 = S\n", - " ! Time = 1.81s, Average fail depth = 10, Memory usage = 8.5 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 75531.95 13000 1 1 F 9999027 = H\n", - " 75531.95 13000 1 2 F 9999043 = S\n", - " 75531.95 59000 1 3 F 1 != S\n", - " 75531.95 60000 1 3 F 3244980 = S\n", - " 75531.95 61000 1 3 -\n", - " 75531.95 14000 1 4 F 9998982 = H\n", - " 75531.95 62000 1 3 -\n", - " 75531.95 14000 1 1 F 9998976 = S\n", - " 75531.95 63000 1 3 7 = S\n", - " 75531.95 14000 1 2 F 9998962 = H\n", - " 75531.95 64000 1 3 4 != S\n", - " 75531.95 65000 1 3 F 8934996 = H\n", - " 75531.95 15000 1 4 F 124628 = H\n", - " 75531.95 66000 1 3 F 3115582 = H\n", - " 75531.95 67000 1 3 -\n", - " 75531.95 15000 1 1 F 5075794 = H\n", - " 75531.95 15000 1 2 F 9998900 = H\n", - " 75531.95 68000 1 3 F 9999999 = S\n", - " * 75549.55 68654 2.13s 3 (gap > 10000%)\n", - " 75549.55 69000 1 3 -\n", - " ! Time = 2.13s, Average fail depth = 10, Memory usage = 9.3 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 75549.55 16000 1 4 F 5750920 = H\n", - " 75549.55 70000 1 3 F 6308797 = S\n", - " 75549.55 71000 1 3 -\n", - " 75549.55 16000 1 2 F 9998818 = H\n", - " 75549.55 72000 1 3 F 20 = S\n", - " 75549.55 16000 1 1 F 8984206 = S\n", - " 75549.55 73000 1 3 F 31 = H\n", - " 75549.55 74000 1 3 F -\n", - " 75549.55 17000 1 4 F 181 = S\n", - " 75549.55 75000 1 3 -\n", - " 75549.55 76000 1 3 F 19 = H\n", - " 75549.55 77000 1 3 F 3 = S\n", - " 75549.55 17000 1 1 F 242 = H\n", - " 75549.55 17000 1 2 229 != S\n", - " * 75565.00 77422 2.40s 3 (gap > 10000%)\n", - " * 75576.05 77463 2.40s 3 (gap > 10000%)\n", - " 75576.05 78000 1 3 F 2528703 = H\n", - " 75576.05 18000 1 4 211 != H\n", - " * 75614.35 78064 2.44s 3 (gap > 10000%)\n", - " 75614.35 79000 1 3 9039756 != S\n", - " ! Time = 2.44s, Average fail depth = 10, Memory usage = 9.8 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 75614.35 80000 1 3 F 31 = H\n", - " 75614.35 81000 1 3 -\n", - " 75614.35 18000 1 1 172 != S\n", - " 75614.35 18000 1 2 514777 != S\n", - " 75614.35 82000 1 3 F 1457007 = H\n", - " 75614.35 19000 1 4 2696977 != H\n", - " * 75857.80 82459 2.55s 3 (gap > 10000%)\n", - " * 75872.80 82704 2.55s 3 (gap > 10000%)\n", - " 75872.80 83000 1 3 20 != S\n", - " 75872.80 84000 1 3 F 9332940 = S\n", - " 75872.80 85000 1 3 F -\n", - " 75872.80 86000 1 3 9999982 != H\n", - " 75872.80 19000 1 1 5901549 != S\n", - " 75872.80 19000 1 2 F 7473052 = S\n", - " 75872.80 87000 1 3 F 5280468 = H\n", - " 75872.80 20000 1 4 2280415 != H\n", - " 75872.80 88000 1 3 F -\n", - " * 75880.75 19611 2.70s 2 (gap > 10000%)\n", - " * 75883.99 88706 2.74s 3 (gap > 10000%)\n", - " 75883.99 89000 1 3 4940304 != S\n", - " ! Time = 2.74s, Average fail depth = 10, Memory usage = 10.1 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 75892.90 19921 2.74s 2 (gap > 10000%)\n", - " 75892.90 90000 1 3 F -\n", - " 75892.90 20000 1 1 F 5898206 = H\n", - " 75892.90 20000 1 2 278 != S\n", - " 75892.90 21000 1 4 8314684 != H\n", - " * 75914.05 20029 2.77s 2 (gap > 10000%)\n", - " * 75943.78 90741 2.79s 3 (gap > 10000%)\n", - " 75943.78 91000 1 3 -\n", - " 75943.78 92000 1 3 F 9999997 = S\n", - " 75943.78 93000 1 3 F -\n", - " * 75952.43 21624 2.84s 4 (gap > 10000%)\n", - " 75952.43 94000 1 3 9999985 != S\n", - " 75952.43 22000 1 4 6513523 != S\n", - " 75952.43 21000 1 2 270 != S\n", - " * 75959.63 21051 2.87s 2 (gap > 10000%)\n", - " 75959.63 95000 1 3 F 9999970 = S\n", - " 75959.63 21000 1 1 1194115 != H\n", - " 75959.63 96000 1 3 F -\n", - " * 75989.35 96758 2.95s 3 (gap > 10000%)\n", - " 75989.35 97000 1 3 -\n", - " ! Time = 2.95s, Average fail depth = 10, Memory usage = 11.1 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 76000.33 21873 2.98s 2 (gap > 10000%)\n", - " 76000.33 22000 1 2 8325473 != H\n", - " 76000.33 98000 1 3 F -\n", - " 76000.33 23000 1 4 8111637 != H\n", - " 76000.33 99000 1 3 F 17 = H\n", - " * 76003.18 21788 3.07s 1 (gap > 10000%)\n", - " 76003.18 22000 1 1 F 1490162 = S\n", - " 76003.18 100k 1 3 F 9999994 = S\n", - " * 76008.70 22325 3.08s 2 (gap > 10000%)\n", - " 76008.70 101k 1 3 F 4225128 = H\n", - " * 76036.15 23328 3.14s 4 (gap > 10000%)\n", - " * 76042.15 22613 3.14s 2 (gap > 10000%)\n", - " 76042.15 102k 1 3 -\n", - " 76042.15 23000 1 2 F 3303651 = H\n", - " 76042.15 103k 1 3 F 2000107 = H\n", - " * 76049.99 23792 3.24s 4 (gap > 10000%)\n", - " * 76071.49 22546 3.24s 1 (gap > 10000%)\n", - " 76071.49 104k 1 3 -\n", - " 76071.49 24000 1 4 F 266 = S\n", - " * 76071.59 24090 3.32s 4 (gap > 10000%)\n", - " ! Time = 3.32s, Average fail depth = 10, Memory usage = 11.5 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 76098.83 104k 3.35s 3 (gap > 10000%)\n", - " 76098.83 105k 1 3 F 0 = S\n", - " 76098.83 23000 1 1 F 291 = H\n", - " * 76110.25 23351 3.35s 2 (gap > 10000%)\n", - " 76110.25 106k 1 3 F -\n", - " * 76121.35 23270 3.45s 1 (gap > 10000%)\n", - " * 76133.59 23322 3.45s 1 (gap > 10000%)\n", - " 76133.59 24000 1 2 F 9998377 = H\n", - " 76133.59 107k 1 3 13 != H\n", - " 76133.59 25000 1 4 F 5629531 = H\n", - " 76133.59 108k 1 3 F 1872751 = H\n", - " * 76147.33 24137 3.54s 2 (gap > 10000%)\n", - " 76147.33 109k 1 3 -\n", - " * 76163.20 109k 3.58s 3 (gap > 10000%)\n", - " * 76175.63 109k 3.63s 3 (gap > 10000%)\n", - " 76175.63 110k 1 3 F -\n", - " 76175.63 24000 1 1 F 285 = H\n", - " * 76217.89 110k 3.66s 3 (gap > 10000%)\n", - " 76217.89 111k 1 3 F 0 = S\n", - " * 76251.50 111k 3.66s 3 (gap > 10000%)\n", - " ! Time = 3.66s, Average fail depth = 10, Memory usage = 12.0 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76251.50 26000 1 4 5631723 != H\n", - " 76251.50 25000 1 2 6818556 != S\n", - " * 76262.14 111k 3.72s 3 (gap > 10000%)\n", - " 76262.14 112k 1 3 F 9999986 = H\n", - " * 76283.83 112k 3.72s 3 (gap > 10000%)\n", - " 76283.83 113k 1 3 F 9999993 = S\n", - " 76283.83 114k 1 3 25 != H\n", - " 76283.83 25000 1 1 F 732592 = H\n", - " 76283.83 115k 1 3 -\n", - " * 76293.08 115k 3.84s 3 (gap > 10000%)\n", - " * 76314.88 115k 3.88s 3 (gap > 10000%)\n", - " * 76344.65 115k 3.88s 3 (gap > 10000%)\n", - " 76344.65 27000 1 4 9998238 != S\n", - " 76344.65 26000 1 2 F 960649 = H\n", - " * 76357.13 115k 3.92s 3 (gap > 10000%)\n", - " 76357.13 116k 1 3 9999998 != H\n", - " 76357.13 117k 1 3 F 23 = S\n", - " 76357.13 26000 1 1 6785591 != S\n", - " 76357.13 118k 1 3 9999988 != H\n", - " * 76373.14 118k 4.05s 3 (gap > 10000%)\n", - " ! Time = 4.05s, Average fail depth = 10, Memory usage = 12.4 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76373.14 119k 1 3 -\n", - " 76373.14 120k 1 3 F 2 = H\n", - " 76373.14 27000 1 2 F 9998206 = H\n", - " 76373.14 28000 1 4 F 270 = S\n", - " 76373.14 121k 1 3 F 301 = S\n", - " * 76400.35 121k 4.16s 3 (gap > 10000%)\n", - " 76400.35 122k 1 3 F -\n", - " 76400.35 27000 1 1 9998145 != H\n", - " 76400.35 123k 1 3 9999993 != H\n", - " 76400.35 124k 1 3 F 4 = H\n", - " 76400.35 29000 1 4 F 4774230 = S\n", - " 76400.35 28000 1 2 F 9998113 = S\n", - " 76400.35 125k 1 3 12 != S\n", - " 76400.35 126k 1 3 -\n", - " 76400.35 127k 1 3 F -\n", - " 76400.35 28000 1 1 F 280 = S\n", - " 76400.35 128k 1 3 F 31 = S\n", - " 76400.35 129k 1 3 13 != S\n", - " 76400.35 30000 1 4 8827077 != S\n", - " 76400.35 29000 1 2 7800255 != S\n", - " ! Time = 4.47s, Average fail depth = 10, Memory usage = 13.3 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76400.35 130k 1 3 6 = S\n", - " * 76426.28 130k 4.59s 3 (gap > 10000%)\n", - " 76426.28 131k 1 3 F 0 = S\n", - " 76426.28 29000 1 1 F 3974289 = H\n", - " 76426.28 31000 1 4 F 1897016 = H\n", - " 76426.28 30000 1 2 5520495 != S\n", - " 76426.28 132k 1 3 F -\n", - " * 76517.20 132k 4.66s 3 (gap > 10000%)\n", - " 76517.20 133k 1 3 7436432 != H\n", - " * 76536.73 133k 4.67s 3 (gap > 10000%)\n", - " 76536.73 134k 1 3 -\n", - " 76536.73 30000 1 1 232589 != S\n", - " 76536.73 135k 1 3 F 4 = H\n", - " 76536.73 31000 1 2 F 1558561 = H\n", - " 76536.73 136k 1 3 0 != S\n", - " 76536.73 32000 1 4 F 9997951 = H\n", - " 76536.73 137k 1 3 F 31 = H\n", - " * 76554.98 137k 4.91s 3 (gap > 10000%)\n", - " 76554.98 138k 1 3 F 2 = H\n", - " 76554.98 139k 1 3 -\n", - " ! Time = 4.98s, Average fail depth = 10, Memory usage = 13.6 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76554.98 33000 1 4 F 9997866 = S\n", - " 76554.98 31000 1 1 282 != S\n", - " 76554.98 32000 1 2 F 9997909 = H\n", - " 76554.98 140k 1 3 F 1 = H\n", - " 76554.98 141k 1 3 22 != S\n", - " * 76571.95 141k 5.13s 3 (gap > 10000%)\n", - " 76571.95 142k 1 3 283 != H\n", - " 76571.95 143k 1 3 -\n", - " 76571.95 32000 1 1 9997839 != H\n", - " 76571.95 144k 1 3 F 13 = H\n", - " 76571.95 34000 1 4 293 != S\n", - " * 76582.39 34005 5.21s 4 (gap > 10000%)\n", - " 76582.39 33000 1 2 F 9997830 = H\n", - " * 76601.15 34105 5.27s 4 (gap > 10000%)\n", - " 76601.15 145k 1 3 282 != H\n", - " 76601.15 146k 1 3 F -\n", - " * 76610.50 34643 5.35s 4 (gap > 10000%)\n", - " 76610.50 147k 1 3 F 852341 = S\n", - " * 76627.03 147k 5.38s 3 (gap > 10000%)\n", - " * 76642.55 147k 5.38s 3 (gap > 10000%)\n", - " ! Time = 5.38s, Average fail depth = 10, Memory usage = 14.0 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76642.55 33000 1 1 F 9997809 = S\n", - " 76642.55 148k 1 3 -\n", - " * 76654.90 148k 5.42s 3 (gap > 10000%)\n", - " 76654.90 35000 1 4 F 306 = H\n", - " 76654.90 149k 1 3 F 22 = H\n", - " 76654.90 34000 1 2 F 296 = H\n", - " 76654.90 150k 1 3 F 9999969 = H\n", - " * 76667.08 150k 5.52s 3 (gap > 10000%)\n", - " 76667.08 151k 1 3 F 9999995 = H\n", - " 76667.08 34000 1 1 F 9997733 = S\n", - " * 76675.75 151k 5.62s 3 (gap > 10000%)\n", - " 76675.75 152k 1 3 F 0 = H\n", - " 76675.75 36000 1 4 292 != S\n", - " 76675.75 35000 1 2 284 != S\n", - " 76675.75 153k 1 3 F 19 = S\n", - " 76675.75 154k 1 3 F 4 = H\n", - " 76675.75 155k 1 3 F -\n", - " * 76679.35 36925 5.77s 4 (gap > 10000%)\n", - " 76679.35 37000 1 4 7540446 != S\n", - " 76679.35 35000 1 1 9997692 != H\n", - " ! Time = 5.77s, Average fail depth = 10, Memory usage = 14.4 MB\n", - " ! Current bound is 1.000310e+13 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76679.35 156k 1 3 F 9999992 = H\n", - " 76679.35 36000 1 2 F 293 = H\n", - " 76679.35 157k 1 3 F 31 = S\n", - " 76679.35 158k 1 3 F -\n", - " 76679.35 159k 1 3 -\n", - " 76679.35 38000 1 4 361 != H\n", - " 76679.35 36000 1 1 F 294 = S\n", - " 76679.35 160k 1 3 2314097 != S\n", - " 76679.35 37000 1 2 9997574 != S\n", - " 76679.35 161k 1 3 8760822 != S\n", - " 76679.35 162k 1 3 23 != H\n", - " 76679.35 163k 1 3 F 31 = S\n", - " 76679.35 164k 1 3 9999970 != S\n", - " 76679.35 39000 1 4 F 448 = H\n", - " 76679.35 165k 1 3 -\n", - " 76679.35 37000 1 1 F 6657372 = H\n", - " 76679.35 166k 1 3 F 3946955 = H\n", - " 76679.35 38000 1 2 F 503 = S\n", - " 76679.35 167k 1 3 F 305 = S\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 5.001550e+12 (gap > 10000%)\n", - " ! Time = 6.45s, Average fail depth = 10, Memory usage = 15.2 MB\n", - " ! Current bound is 5.001550e+12 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 2.500775e+12 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 1.250387e+12 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 6.251938e+11 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 3.125969e+11 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 1.562985e+11 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 7.814929e+10 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 3.907468e+10 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 1.953738e+10 (gap > 10000%)\n", - " 76679.35 38409 2 2 F -\n", - " + New bound is 9.768729e+09 (gap > 10000%)\n", - " 76679.35 38000 1 1 6743607 != H\n", - " 76679.35 168k 1 3 -\n", - " 76679.35 40000 1 4 100922 = H\n", - " 76679.35 169k 1 3 F -\n", - " 76679.35 170k 1 3 -\n", - " 76679.35 39000 1 2 F 633 = H\n", - " 76679.35 171k 1 3 9999997 != H\n", - " 76679.35 172k 1 3 F 9999988 = S\n", - " 76679.35 41000 1 4 F 619 = S\n", - " 76679.35 173k 1 3 1191495 != H\n", - " 76679.35 39000 1 1 F 855432 = S\n", - " ! Time = 6.82s, Average fail depth = 10, Memory usage = 15.5 MB\n", - " ! Current bound is 9.768729e+09 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76679.35 174k 1 3 -\n", - " 76679.35 175k 1 3 F 1403757 = S\n", - " 76679.35 40000 2 2 F 689 = H\n", - " 76679.35 176k 1 3 1 != S\n", - " 76679.35 40093 2 2 F -\n", - " + New bound is 4.884403e+09 (gap > 10000%)\n", - " 76679.35 177k 1 3 -\n", - " 76679.35 42000 1 4 F 583 = H\n", - " 76679.35 178k 1 3 6 = H\n", - " 76679.35 40000 1 1 F 4886106 = S\n", - " 76679.35 179k 1 3 F 9999972 = S\n", - " 76679.35 41000 1 2 F 748 = S\n", - " 76679.35 180k 1 3 -\n", - " 76679.35 181k 1 3 2 != H\n", - " 76679.35 43000 1 4 588 != H\n", - " 76679.35 182k 1 3 -\n", - " 76679.35 41000 1 1 4408031 != S\n", - " 76679.35 183k 1 3 0 != H\n", - " 76679.35 184k 1 3 F 8658197 = S\n", - " 76679.35 42000 2 2 F 822 = S\n", - " 76679.35 185k 1 3 -\n", - " ! Time = 7.36s, Average fail depth = 10, Memory usage = 16.0 MB\n", - " ! Current bound is 4.884403e+09 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76679.35 186k 1 3 F 9999990 = S\n", - " 76679.35 42000 1 1 F 615 = S\n", - " 76679.35 187k 1 3 -\n", - " 76679.35 44000 1 4 5990681 != H\n", - " 76679.35 188k 1 3 F 0 = S\n", - " 76679.35 189k 1 3 -\n", - " 76679.35 43000 1 2 603 != S\n", - " 76679.35 190k 1 3 7915623 != S\n", - " 76679.35 191k 1 3 F 9999970 = S\n", - " 76679.35 192k 1 3 F -\n", - " 76679.35 43000 1 1 F 692 = S\n", - " 76679.35 45000 1 4 F 2741451 = S\n", - " 76679.35 44000 1 2 2563347 != H\n", - " 76679.35 193k 1 3 -\n", - " 76679.35 46000 1 4 5193648 != H\n", - " 76679.35 45000 1 2 2209 != S\n", - " 76679.35 47000 1 4 686 != H\n", - " 76679.35 44000 1 1 2189635 != H\n", - " 76679.35 45000 1 1 F 544 = S\n", - " 76679.35 46000 2 2 890 != S\n", - " ! Time = 7.89s, Average fail depth = 10, Memory usage = 16.4 MB\n", - " ! Current bound is 4.884403e+09 (gap > 10000%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 76679.35 194k 1 3 F 6 = S\n", - " 76679.35 195k 1 3 F 1 = S\n", - " 76679.35 48000 1 4 F 2206 = S\n", - " 76679.35 46000 1 1 F 1246 = S\n", - " 76679.35 196k 1 3 1 != S\n", - " 76679.35 47000 2 1 F 1746 = S\n", - " 76679.35 47000 1 2 F 572 = H\n", - " 76679.35 48264 1 4 -\n", - " + New bound is 76687.02 (gap is 0.01%)\n", - " ! ----------------------------------------------------------------------------\n", - " ! Search completed, 136 solutions found.\n", - " ! Best objective : 76679.35 (optimal - effective tol. is 7.66794)\n", - " ! Best bound : 76687.02\n", - " ! ----------------------------------------------------------------------------\n", - " ! Number of branches : 339108\n", - " ! Number of fails : 169893\n", - " ! Total memory usage : 16.9 MB (16.9 MB CP Optimizer + 0.0 MB Concert)\n", - " ! Time spent in solve : 7.98s (7.98s engine + 0.00s extraction)\n", - " ! Search speed (br. / s) : 42494.7\n", - " ! ----------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "from docplex.cp.model import CpoModel as Model\n", - "\n", - "m = Model() \n", - "\n", - "Ps = m.integer_var(name='S')\n", - "Ph = m.integer_var(name='H')\n", - "\n", - "DS = (222 - 0.6*Ps + 0.35*Ph)\n", - "DH = (270 + 0.1*Ps - 0.64*Ph)\n", - "\n", - "m.add_constraint(Ps >= 0)\n", - "m.add_constraint(Ph >= 0)\n", - "m.add_constraint(Ph <= 10e6)\n", - "m.add_constraint(Ps <= 10e6)\n", - "\n", - "m.maximize(Ph * DH + Ps * DS)\n", - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "316 Labor\n", - "303 Capital\n" - ] - } - ], - "source": [ - "print(solution[Ph],\" Labor\") ### Answer is also close enough lmao\n", - "print(solution[Ps],\" Capital\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "76679.35999999996\n" - ] - } - ], - "source": [ - "print(solution.get_objective_value())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/ProductPricing_Problem.ipynb b/Non Linear Optimization Models/ProductPricing_Problem.ipynb deleted file mode 100644 index b84810f..0000000 --- a/Non Linear Optimization Models/ProductPricing_Problem.ipynb +++ /dev/null @@ -1,148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Phillips Inc. produces two distinct products, A and B. The products\n", - "do not compete with each other in the marketplace; that is, neither cost, price, nor\n", - "demand for one product will impact the demand for the other. Phillips’ analysts have\n", - "collected data on the effects of advertising on profits. These data suggest that, although\n", - "higher advertising correlates with higher profits, the marginal increase in profits dimin-\n", - "ishes at higher advertising levels, particularly for product B. Analysts have estimated\n", - "the following functions:\n", - "\n", - "Annual profit for product A = $1.2712LN(X_A) + 17.414$\n", - "\n", - "Annual profit for product B = $0.3970LN(X_B) + 16.109$\n", - "\n", - "where XA and XB are the advertising amount allocated to products A and B, respec-\n", - "tively, in thousands of dollars, profit is in millions of dollars, and LN is the natural log-\n", - "arithm function. The advertising budget is $500,000, and management has dictated that\n", - "at least $50,000 must be allocated to each of the two products.\n", - "(Hint: To compute a natural logarithm for the value X in Excel, use the formula =LN(X).\n", - "For Solver to find an answer, you also need to start with decision variable values\n", - "greater than 0 in this problem.)\n", - "\n", - "a. Build an optimization model that will prescribe how Phillips should allocate its\n", - "marketing budget to maximize profit.\n", - "\n", - "b. Solve the model you constructed in part (a) using Excel Solver." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ! --------------------------------------------------- CP Optimizer 22.1.1.0 --\n", - " ! Maximization problem - 2 variables, 3 constraints\n", - " ! Initial process time : 0.00s (0.00s extraction + 0.00s propagation)\n", - " ! . Log search space : 37.2 (before), 37.2 (after)\n", - " ! . Memory usage : 267.0 kB (before), 267.0 kB (after)\n", - " ! Using parallel search with 4 workers.\n", - " ! ----------------------------------------------------------------------------\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 0 2 -\n", - " + New bound is 55.23796\n", - " 0 2 1 -\n", - " + New bound is 54.64008\n", - " * 51.57255 802 0.04s 1 (gap is 5.95%)\n", - " * 53.40427 804 0.04s 1 (gap is 2.31%)\n", - " * 53.42670 806 0.04s 1 (gap is 2.27%)\n", - " * 53.76813 815 0.04s 1 (gap is 1.62%)\n", - " * 54.25430 817 0.04s 1 (gap is 0.71%)\n", - " * 54.25973 821 0.04s 1 (gap is 0.70%)\n", - " * 54.36566 822 0.04s 1 (gap is 0.50%)\n", - " 54.36566 1000 1 1 50778 != B Ads\n", - " * 54.37372 804 0.04s 2 (gap is 0.49%)\n", - " * 54.43548 815 0.04s 2 (gap is 0.38%)\n", - " 54.43548 1000 1 2 F 60527 = B Ads\n", - " * 54.44091 210 0.04s 3 (gap is 0.37%)\n", - " * 54.47656 232 0.04s 3 (gap is 0.30%)\n", - " * 54.48201 253 0.04s 3 (gap is 0.29%)\n", - " * 54.48746 274 0.04s 3 (gap is 0.28%)\n", - " * 54.49291 295 0.04s 3 (gap is 0.27%)\n", - " ! ----------------------------------------------------------------------------\n", - " ! Search completed, 14 solutions found.\n", - " ! Best objective : 54.49291 (optimal - effective tol. is 0.00544929)\n", - " ! Best bound : 54.64008\n", - " ! ----------------------------------------------------------------------------\n", - " ! Number of branches : 3299\n", - " ! Number of fails : 1635\n", - " ! Total memory usage : 2.8 MB (2.8 MB CP Optimizer + 0.0 MB Concert)\n", - " ! Time spent in solve : 0.04s (0.04s engine + 0.00s extraction)\n", - " ! Search speed (br. / s) : 109966.7\n", - " ! ----------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "from docplex.cp.model import CpoModel as Model\n", - "import numpy as np\n", - "\n", - "m = Model(name='buses')\n", - "\n", - "XA = m.integer_var(name='A Ads')\n", - "XB = m.integer_var(name='B Ads')\n", - "\n", - "m.add_constraint(XA >= 50000)\n", - "m.add_constraint(XB >= 50000)\n", - "m.add_constraint(XA + XB <= 500000)\n", - "\n", - "A_profit = 1.2712 * m.log(XA) + 17.414\n", - "B_profit = 0.3970 * m.log(XB) + 16.109\n", - "\n", - "m.maximize(A_profit + B_profit)\n", - "\n", - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "397558 A ads\n", - "102442 B ads\n", - "54.492913449909864 profit\n" - ] - } - ], - "source": [ - "print(solution[XA],\" A ads\")\n", - "print(solution[XB],\" B ads\") \n", - "print(solution.get_objective_value(),\" profit\") # Not as close, but good enough" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/ProductionPlanning_Problem.ipynb b/Non Linear Optimization Models/ProductionPlanning_Problem.ipynb deleted file mode 100644 index 0aeaedf..0000000 --- a/Non Linear Optimization Models/ProductionPlanning_Problem.ipynb +++ /dev/null @@ -1,147 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The profit function for two products is\n", - "\n", - "$Profit = -3 x^2_1 + 42 x_1 - 3 x^2_2 + 48x_2 + 700$\n", - "\n", - "where x1 represents units of production of product 1 and x 2 represents units of produc-\n", - "tion of product 2. \n", - "\n", - "Producing one unit of product 1 requires 4 labor-hours and producing\n", - "one unit of product 2 requires 6 labor-hours. Currently, 24 labor-hours are available.\n", - "\n", - "The cost of labor-hours is already factored into the profit function, but it is possible to\n", - "schedule overtime at a premium of $5 per hour.\n", - "\n", - "a. Formulate an optimization problem that can be used to find the optimal produc-\n", - "tion quantity of products 1 and 2 and the optimal number of overtime hours to schedule.\n", - "\n", - "b. Solve the optimization model you formulated in part (a). How much should be\n", - "­produced and how many overtime hours should be scheduled?" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "x1 = m.integer_var(name='x1')\n", - "x2 = m.integer_var(name='x2')\n", - "\n", - "xtra_time = m.continuous_var(name='extra_time')\n", - "\n", - "m.add_constraint(4*x1 + 6*x2 <= 24 + xtra_time)\n", - "\n", - "Profit = -3 * x1**2 + 42 * x1 - 3 * x2**2 + 48 * x2 + 700 - xtra_time * 5\n", - "\n", - "m.maximize(Profit)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Found incumbent of value 700.000000 after 0.00 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Reduced MIQP has 1 rows, 3 columns, and 3 nonzeros.\n", - "Reduced MIQP has 0 binaries, 2 generals, 0 SOSs, and 0 indicators.\n", - "Reduced MIQP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.01 sec. (0.00 ticks)\n", - "Tried aggregator 1 time.\n", - "Reduced MIQP has 1 rows, 3 columns, and 3 nonzeros.\n", - "Reduced MIQP has 0 binaries, 2 generals, 0 SOSs, and 0 indicators.\n", - "Reduced MIQP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.01 sec. (0.00 ticks)\n", - "Classifier predicts products in MIQP should be linearized.\n", - "MIP emphasis: balance optimality and feasibility.\n", - "MIP search method: dynamic search.\n", - "Parallel mode: deterministic, using up to 4 threads.\n", - "Root relaxation solution time = 0.01 sec. (0.02 ticks)\n", - "\n", - " Nodes Cuts/\n", - " Node Left Objective IInf Best Integer Best Bound ItCnt Gap\n", - "\n", - "* 0+ 0 700.0000 0.00%\n", - " 0 0 887.3333 1 700.0000 887.3333 16 26.76%\n", - "* 0+ 0 886.0000 887.3333 0.15%\n", - "* 0+ 0 887.0000 887.3333 0.04%\n", - " 0 0 cutoff 887.0000 887.3333 16 0.04%\n", - "Elapsed time = 0.08 sec. (0.04 ticks, tree = 0.01 MB, solutions = 3)\n", - "\n", - "Root node processing (before b&c):\n", - " Real time = 0.08 sec. (0.04 ticks)\n", - "Parallel b&c, 4 threads:\n", - " Real time = 0.00 sec. (0.00 ticks)\n", - " Sync time (average) = 0.00 sec.\n", - " Wait time (average) = 0.00 sec.\n", - " ------------\n", - "Total (root+branch&cut) = 0.08 sec. (0.04 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model5\n", - "objective: 887\n", - "status: OPTIMAL_SOLUTION(2)\n", - "x1=4\n", - "x2=3\n", - "extra_time=10.000\n", - "\n" - ] - } - ], - "source": [ - "print(solution) ### SOlution is almost right, Good enough in my book. However, the formlualtion is correct, just not integer." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/SteelProduction_Problem.ipynb b/Non Linear Optimization Models/SteelProduction_Problem.ipynb deleted file mode 100644 index 6e49ce9..0000000 --- a/Non Linear Optimization Models/SteelProduction_Problem.ipynb +++ /dev/null @@ -1,217 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let S represent the amount of steel produced (in tons).\n", - "Steel production is related to the amount of labor used (L) and the amount of capital\n", - "used (C) by the following function:\n", - "\n", - "$S = 20 L^{0.30}C^{0.70}$\n", - "\n", - "In this formula L represents the units of labor input and C the units of capital input.\n", - "Each unit of labor costs $50, and each unit of capital costs $100.\n", - "\n", - "a. Formulate an optimization problem that will determine how much labor and capital\n", - "are needed to produce 50,000 tons of steel at minimum cost.\n", - "\n", - "b. Solve the optimization problem you formulated in part (a). (Hint: When using Excel\n", - "Solver, start with an initial L . 0 and C . 0.)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ! --------------------------------------------------- CP Optimizer 22.1.1.0 --\n", - " ! Minimization problem - 2 variables, 1 constraint\n", - " ! Initial process time : 0.00s (0.00s extraction + 0.00s propagation)\n", - " ! . Log search space : 106.0 (before), 106.0 (after)\n", - " ! . Memory usage : 267.0 kB (before), 267.0 kB (after)\n", - " ! Using parallel search with 4 workers.\n", - " ! ----------------------------------------------------------------------------\n", - " ! Best Branches Non-fixed W Branch decision\n", - " 0 2 -\n", - " + New bound is 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 2 1 F -\n", - " + New bound is 374035\n", - " 0 2 1 F -\n", - " + New bound is 374036\n", - " * 1.06032e+13 4 0.04s 1 (gap is 100.00%)\n", - " * 7148350 18 0.04s 1 (gap is 94.77%)\n", - " * 5311300 30 0.04s 1 (gap is 92.96%)\n", - " * 5310750 31 0.04s 1 (gap is 92.96%)\n", - " * 5295650 33 0.04s 1 (gap is 92.94%)\n", - " * 4014750 35 0.04s 1 (gap is 90.68%)\n", - " * 2922550 37 0.04s 1 (gap is 87.20%)\n", - " * 2840700 39 0.04s 1 (gap is 86.83%)\n", - " * 2788150 41 0.04s 1 (gap is 86.58%)\n", - " * 2787850 42 0.04s 1 (gap is 86.58%)\n", - " * 2785100 44 0.04s 1 (gap is 86.57%)\n", - " * 2667650 46 0.04s 1 (gap is 85.98%)\n", - " * 2419300 48 0.04s 1 (gap is 84.54%)\n", - " * 2220450 50 0.04s 1 (gap is 83.15%)\n", - " * 2179300 52 0.04s 1 (gap is 82.84%)\n", - " * 2179050 53 0.04s 1 (gap is 82.83%)\n", - " * 2175400 55 0.04s 1 (gap is 82.81%)\n", - " ! Time = 0.04s, Average fail depth = 3, Memory usage = 1.2 MB\n", - " ! Current bound is 374036 (gap is 82.81%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 2051200 57 0.04s 1 (gap is 81.77%)\n", - " * 1892900 59 0.04s 1 (gap is 80.24%)\n", - " * 1753000 61 0.04s 1 (gap is 78.66%)\n", - " * 1742250 63 0.04s 1 (gap is 78.53%)\n", - " * 1742050 64 0.04s 1 (gap is 78.53%)\n", - " * 1738950 66 0.04s 1 (gap is 78.49%)\n", - " * 1050650 68 0.04s 1 (gap is 64.40%)\n", - " * 862650 70 0.04s 1 (gap is 56.64%)\n", - " * 823900 72 0.04s 1 (gap is 54.60%)\n", - " * 822250 74 0.04s 1 (gap is 54.51%)\n", - " * 822150 75 0.04s 1 (gap is 54.51%)\n", - " * 821750 77 0.04s 1 (gap is 54.48%)\n", - " * 738400 79 0.04s 1 (gap is 49.35%)\n", - " * 573000 81 0.04s 1 (gap is 34.72%)\n", - " * 429700 83 0.04s 1 (gap is 12.95%)\n", - " * 429650 84 0.04s 1 (gap is 12.94%)\n", - " * 429600 85 0.04s 1 (gap is 12.93%)\n", - " * 429500 87 0.04s 1 (gap is 12.91%)\n", - " * 417950 89 0.04s 1 (gap is 10.51%)\n", - " * 416400 91 0.04s 1 (gap is 10.17%)\n", - " ! Time = 0.04s, Average fail depth = 3, Memory usage = 1.4 MB\n", - " ! Current bound is 374036 (gap is 10.17%)\n", - " ! Best Branches Non-fixed W Branch decision\n", - " * 376900 93 0.04s 1 (gap is 0.76%)\n", - " * 376850 94 0.04s 1 (gap is 0.75%)\n", - " * 376800 95 0.04s 1 (gap is 0.73%)\n", - " * 376750 96 0.04s 1 (gap is 0.72%)\n", - " * 376500 98 0.04s 1 (gap is 0.65%)\n", - " * 376300 100 0.04s 1 (gap is 0.60%)\n", - " * 374250 102 0.04s 1 (gap is 0.06%)\n", - " * 374200 103 0.04s 1 (gap is 0.04%)\n", - " * 374150 104 0.04s 1 (gap is 0.03%)\n", - " * 374100 105 0.04s 1 (gap is 0.02%)\n", - " * 374050 106 0.04s 1 (gap is 0.00%)\n", - " ! ----------------------------------------------------------------------------\n", - " ! Search completed, 48 solutions found.\n", - " ! Best objective : 374050 (optimal - effective tol. is 37)\n", - " ! Best bound : 374036\n", - " ! ----------------------------------------------------------------------------\n", - " ! Number of branches : 1274\n", - " ! Number of fails : 639\n", - " ! Total memory usage : 3.1 MB (3.1 MB CP Optimizer + 0.0 MB Concert)\n", - " ! Time spent in solve : 0.04s (0.04s engine + 0.00s extraction)\n", - " ! Search speed (br. / s) : 31850.0\n", - " ! ----------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "from docplex.cp.model import CpoModel as Model\n", - "\n", - "m = Model() \n", - "\n", - "c0 = 5\n", - "c1 = 0.25\n", - "c2 = 0.75\n", - "\n", - "L = m.integer_var(name='Labor')\n", - "C = m.integer_var(name='Capital')\n", - "\n", - "m.add_constraint(20*L**0.3*C**0.7 >= 50000)\n", - "\n", - "m.minimize(50*L + 100*C)\n", - "\n", - "solution = m.solve(log_output=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2245 Labor\n", - "2618 Capital\n" - ] - } - ], - "source": [ - "print(solution[L],\" Labor\")\n", - "print(solution[C],\" Capital\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "50000.41958803582" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "20*solution[L]**0.3*solution[C]**0.7" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "374050\n" - ] - } - ], - "source": [ - "print(solution.get_objective_value())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Non Linear Optimization Models/notes.md b/Non Linear Optimization Models/notes.md deleted file mode 100644 index 2e8599d..0000000 --- a/Non Linear Optimization Models/notes.md +++ /dev/null @@ -1,28 +0,0 @@ -What does this mean >> "Convex region: For any 2 points in the region, any convex combination should be between them"??? - -* Convex combination= l*x_1 + (1-l)* x_2 where l is lambda. -* "Reduces cost" in Non linear is called "Reduced Gradiant". - - -Local and Global Optimality: -* We used to have one name for optimal solution. Just the optimal solution for all the problems -* You can face problems where the optimal solution for the problem in Non Linear is infeasable (Global Optimality is infeasable). -* Hence, we try to look for Local Optimality, meaning the optimal solution whithin the neighborhood for the answer. - -Local Optima: -* A feasible solution when there are no other feasible points with a better objective function value in the neighborhood region. - -Shapes (u (cup), n (cap), __ (line)): -u is convex -n is concave -u, n, and __ all together are convex and concave. - -DOCPLex can't do some of these probelms, hence, you have to derivate based on X = 0 and Y = 0. - -* Forcasting adoption of a new product: - * Inputs: - * M = the number of people estimated to eventually adopt the new product - * q = the coef of imitation - * p = the coef of innovation - * Function: - * F_t = (p + q[C_{t-1}/m])(m-C_{t-1}) diff --git a/README.md b/README.md new file mode 100644 index 0000000..3fc5095 --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +OUT OF EVERYTHING YOU COULDA HAVE SPENT YOUR VALUABLE TIME ON, YOU CHOOSE STOCHASTIC OPTIMIZATION. +MY DISAPPOINTMENT IS IMMEASURABLE AND MY DAY IS RUINED. \ No newline at end of file diff --git a/StandardForms/notes.md b/StandardForms/notes.md deleted file mode 100644 index fdcd075..0000000 --- a/StandardForms/notes.md +++ /dev/null @@ -1,171 +0,0 @@ -Standard Form main features: -* Always Mamimize -* Non-Negative Decision Variables -* Non-Negative Right Hand Side of Constraints -* All constraints should be equality - -How to deal with: -

Minimize

-

Multiply objective function with (-1)

- -____ - -

Non Negative RHS in Constraint

-

Multiply both sides with (-1) and flip the < to >, and vice versa. -For Example: - -$Ax \geq -b$ - -Becomes - -$-Ax \leq b$ -

- -____ - -

Greater than or equal in Constraint (Slack Variable)

-Change constraint from - -$Ax \geq b$ - -Becomes - -$Ax - e = b$ - -Subject to - -$e \geq 0$ - -____ -

Less than or equal in Constraint (Excess Variable)

-Change constraint from - -$Ax \leq b$ - -Becomes - -$Ax + e = b$ - -Subject to - -$e \geq 0$ - -____ - -

Non Postive Decision Variable

-**CHECK THIS AGAIN** - -This: - -$xA = b$ - -$x \leq 0$ - - -Becomes: - -$-x^\prime A = b$ - -$x^\prime \geq 0$ - - -____ - -

Unrestricted Decision Variable

-This: - -$Ax = b$ - -$x\;urs$ (Means Unsrestricred) - -Becomes: - -$x = x^\prime - x^{\prime}{^\prime}$ - -where - -$x^\prime, x^{\prime}{^\prime} \geq 0$ - -So - -$A(x^\prime - x^{\prime}{^\prime}) = b$ - -___ - -Example 1: - -$\mathrm{minimize} -4x_1 + x_2 + x_3$
-Subject To
-$3x_1 + x_2 + x_3 \leq 6$
-$-2x_1 - x_2 + x_3 \leq -3$
-$x_1 + x_2 - x_3 \geq 6$
- -$x_1 \geq 0, \; x_2 \leq 0, \; x_3 \;urs$
- -Becomes: - -$\mathrm{maximize} \; 4x_1 - x_2 - x_3$
-Subject To
-$3x_1 - x_2^\prime + (x_3^{\prime} - x_3^{\prime}{^\prime}) + e_1 = 6$
-$2x_1 - x_2^\prime - (x_3^{\prime} + x_3^{\prime}{^\prime}) - e_2 = 3$
-$x_1 - x_2^\prime - (x_3^{\prime} + x_3^{\prime}{^\prime}) - e_3 = 6$
-$e_1, e_2, e_3 \geq 0$
-$x_2^\prime, x_3^{\prime}, x_3^{\prime}{^\prime} \geq 0$
-$x_1 \geq 0$ - - -___ - -Example 2 (ORIGINAL - ALREADY STANDARIZED - Primal Dakota Problem): - -$\mathrm{maximize} \; 60x_1 + 30x_2 + 20x_3$
-Subject To
-$8x_1 + 6x_2 + x_3 \leq 48$
-$4x_1 + 2x_2 + 1.5x_3 \leq 20$
-$2x_1 + 1.5x_2 + 0.5x_3 \leq 8$
- -$x_1, x_2, x_3 \geq 0$
- -ITS DUAL IS: - -$\mathrm{minimize} \; 48x_1 + 20x_2 + 8x_3$
-Subject To
-$8x_1 + 4x_2 + 2x_3 \geq 60$
-$6x_1 + 2x_2 + 1.5x_3 \geq 30$
-$x_1 + 1.5x_2 + 0.5x_3 \geq 20$
- -$x_1, x_2, x_3 \geq 0$
- -DUAL as in: |Constrains| = |Decision Variables in Dual|
-DUAL as in: B(Right hand side) is in DUAL C(Decision Variable Coef)
-DUAL as in: Max become min in DUAL
-DUAL as in: leq becomes geq in DUAL
-Given a PRIMAL problem with N Decision variables, you have n constrains in Dual.
-Given a PRIMAL problem with M constraints, you have m Decision Variables in Dual. where each constraint in DUAL has M "7ad".
- -Which is done by:
-Primal Problem:
- -$\mathrm{max} \; c^T x$
-subject to
-$Ax \leq b$
-$x \geq 0$
- -Dual Problem -$\mathrm{min} \; b^T y$
-subject to
-$Ay \geq c$
-$y \geq 0$
- - -1. For each constaint we add a DV. -2. Use Bs (Right hand side of constraints as Coefs) -3. DUAL A = We transpose PRIMAL A (Coef of Left hand side constraints) -4. Just check the slides. - * if constraint geq b --> y leq 0 - -____ - - - - diff --git a/Time Series/AdministrativeExpenses_Problem.ipynb b/Time Series/AdministrativeExpenses_Problem.ipynb deleted file mode 100644 index 7c422af..0000000 --- a/Time Series/AdministrativeExpenses_Problem.ipynb +++ /dev/null @@ -1,216 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Seneca Children’s Fund (SCF) is a local charity\n", - "that runs a summer camp for disadvantaged children. The fund’s board of directors\n", - "has been working very hard over recent years to decrease the amount of overhead\n", - "expenses, a major factor in how charities are rated by independent agencies. The\n", - "following data show the percentage of the money SCF has raised that was spent on\n", - "administrative and fund-raising expenses over the past seven years:\n", - "\n", - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "\n", - "b. Use simple linear regression analysis to find the parameters for the line that mini-\n", - "mizes MSE for this time series.\n", - "\n", - "c. Forecast the percentage of administrative expenses for year 8.\n", - "\n", - "d. If SCF can maintain its current trend in reducing administrative expenses, how long\n", - "will it take for SCF to achieve a level of 5% or less?" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "period = [1,2,3,4,5,6,7]\n", - "data = [13.9,12.2,10.5,10.4,11.5,10.0,8.5]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.ylim(bottom = 0, top = max(data)*1.5)\n", - "plt.plot(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Expenses.lp'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "b_0 = m.continuous_var(name = 'Intercept', lb = -10e9) # THE LOWER BOUND IS IMPORTANT\n", - "b_1 = m.continuous_var(name = 'Slope', lb = -10e9)\n", - "\n", - "mse = []\n", - "for i in range(len(period)):\n", - " mse.append((b_0 + b_1 * period[i] - data[i])**2)\n", - "\n", - "m.minimize(sum(mse))\n", - "m.export_as_lp('Expenses.lp')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2022-11-27 | 9160aff4d\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - "Tried aggregator 1 time.\n", - "QP Presolve added 0 rows and 2 columns.\n", - "Reduced QP has 2 rows, 4 columns, and 5 nonzeros.\n", - "Reduced QP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.03 sec. (0.00 ticks)\n", - "Parallel mode: using up to 16 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 16\n", - " Rows in Factor = 2\n", - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 3.9308415e+21 -3.9308415e+21 2.86e-06 0.00e+00 1.43e+12\n", - " 1 2.0545615e+05 -1.7195938e+13 1.42e-06 0.00e+00 9.78e-05\n", - " 2 9.3767044e+00 -8.5979689e+10 9.06e-07 0.00e+00 4.89e-07\n", - " 3 4.2403102e+00 -4.2989844e+08 9.16e-07 0.00e+00 2.44e-09\n", - " 4 4.2398816e+00 -2.1494880e+06 3.48e-07 0.00e+00 1.24e-11\n", - " 5 4.2397377e+00 -1.0743221e+04 7.16e-07 0.00e+00 1.33e-13\n", - " 6 4.2397278e+00 -4.9497305e+01 9.17e-07 0.00e+00 1.89e-14\n", - " 7 4.2397278e+00 4.2400000e+00 9.18e-07 0.00e+00 1.24e-24\n", - "Barrier time = 0.08 sec. (0.01 ticks)\n", - "\n", - "Total time on 16 threads = 0.08 sec. (0.01 ticks)\n" - ] - } - ], - "source": [ - "pp = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model11\n", - "objective: 4.24\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Intercept=13.800\n", - "Slope=-0.700\n", - "\n" - ] - } - ], - "source": [ - "print(pp)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "objective: 4.240\n", - "status: OPTIMAL_SOLUTION(2)\n", - " Intercept=13.800\n", - " Slope=-0.700\n" - ] - } - ], - "source": [ - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/BondInterests_Problem.ipynb b/Time Series/BondInterests_Problem.ipynb deleted file mode 100644 index 9b3a678..0000000 --- a/Time Series/BondInterests_Problem.ipynb +++ /dev/null @@ -1,106 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Corporate triple A bond interest rates for 12 consecutive months\n", - "are as follows:\n", - "\n", - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "\n", - "b. Develop three-month and four-month moving averages for this time series. Does the\n", - "three-month or the four-month moving average provide the better forecasts based on\n", - "MSE? Explain.\n", - "\n", - "c. What is the moving average forecast for the next month?" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = [9.5, 9.3, 9.4, 9.6, 9.8, 9.7, 9.8, 10.5, 9.9, 9.7, 9.6, 9.6]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.ylim(bottom = 0, top = max(data)*1.5)\n", - "plt.plot(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1385937500000001\n", - "0.12024691358024692\n" - ] - } - ], - "source": [ - "def calc_moving_avg(data, k):\n", - " preds = []\n", - " for i in range(k, len(data)+1):\n", - " preds.append(sum(data[i-k:i]) / len(data[i-k:i]))\n", - "\n", - " return preds\n", - "\n", - "four_month_avg = calc_moving_avg(data, 4)\n", - "three_month_avg = calc_moving_avg(data, 3)\n", - "\n", - "mse = lambda X, Y: sum([(i-u)**2 for i, u in zip(X, Y)])/len(X)\n", - "\n", - "print(mse(four_month_avg[:-1], data[4:]))\n", - "print(mse(three_month_avg[:-1], data[3:]))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/DairyProducts_Problem.ipynb b/Time Series/DairyProducts_Problem.ipynb deleted file mode 100644 index 1433f15..0000000 --- a/Time Series/DairyProducts_Problem.ipynb +++ /dev/null @@ -1,122 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Demand for Dairy Products. United Dairies, Inc. supplies milk to several indepen-\n", - "dent grocers throughout Dade County, Florida. Managers at United Dairies want to\n", - "develop a forecast of the number of half gallons of milk sold per week. Sales data for\n", - "the past 12 weeks are as follows:\n", - "\n", - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "b. Use exponential smoothing with a=0.4 to develop a forecast of demand for week 13. What is the resulting MSE" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sales = [2750,3100,3250,2800,2900,3050,3300,3100,2950,3000,3200,3150]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.ylim(bottom = 0, top = max(sales)*1.5)\n", - "plt.plot(sales)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44453.34536482963" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def calc_exponential_smoothing(data, alpha):\n", - " preds = [data[0]]\n", - " for i in range(1, len(data)):\n", - " preds.append(alpha * data[i] + (1 - alpha) * preds[i-1])\n", - " return preds\n", - "\n", - "preds = calc_exponential_smoothing(sales, 0.4)\n", - "week_13_pred = preds[-1]\n", - "\n", - "mse = sum([(i - u)**2 for i, u in zip(preds[:-1], sales[1:])])/len(sales[1:])\n", - "mse" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3117.0075248639996" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "week_13_pred" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/ForecastingMonthlyData_Problem.ipynb b/Time Series/ForecastingMonthlyData_Problem.ipynb deleted file mode 100644 index e03d0af..0000000 --- a/Time Series/ForecastingMonthlyData_Problem.ipynb +++ /dev/null @@ -1,166 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "values = [24, 13, 20, 12, 19, 23, 15]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "\n", - "b. Develop a three-week moving average for this time series. Compute MSE and a\n", - "forecast for week 8.\n", - "\n", - "c. Use a=0.2 to compute the exponential smoothing values for the time series.\n", - "­Compute MSE and a forecast for week 8.\n", - "\n", - "d. Compare the three-week moving average forecast with the exponential smoothing fore-\n", - "cast using a 5 0.2. Which appears to provide the better forecast based on MSE?\n", - "\n", - "e. Use trial and error to find a value of the exponential smoothing coefficient a that\n", - "results in a smaller MSE than what you calculated for a 5 0.2." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.plot(values)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27.5\n" - ] - }, - { - "data": { - "text/plain": [ - "19.0" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3 week moving average\n", - "\n", - "def calc_moving_avg(data, k):\n", - " preds = []\n", - " for i in range(k, len(data)+1):\n", - " preds.append(sum(data[i-k: i])/k)\n", - "\n", - " return preds\n", - "\n", - "preds = calc_moving_avg(values, 3)\n", - "mse = sum([(i - u)**2 for i, u in zip(preds[:-1], values[3:])])/len(values[3:])\n", - "print(mse)\n", - "preds[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "39.845963415816655" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def calc_exponential_smoothing(data, alpha):\n", - " preds = [data[0]]\n", - " for i in range(1, len(data)):\n", - " preds.append(alpha * data[i] + (1- alpha) * preds[i-1])\n", - "\n", - " return preds\n", - "\n", - "preds = calc_exponential_smoothing(values, 0.3)\n", - "\n", - "sum([(i - u)**2 for i, u in zip(preds[:-1], values[1:])]) / len(values[1:])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/Gasoline_Problem.ipynb b/Time Series/Gasoline_Problem.ipynb deleted file mode 100644 index 3276524..0000000 --- a/Time Series/Gasoline_Problem.ipynb +++ /dev/null @@ -1,174 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Moving Average" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Refer to the gasoline sales time\n", - "series data in Table 8.1.\n", - "\n", - "a. Compute four-week and five-week moving averages for the time series.
\n", - "b. Compute the MSE for the four-week and five-week moving average forecasts.
\n", - "c. What appears to be the best number of weeks of past data (three, four, or five) to use\n", - "in the moving average computation? Recall that the MSE for the three-week moving\n", - "average is 10.22." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "week= [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", - "sales= [17, 21, 19, 23, 18, 16, 20, 18, 22, 20, 15, 22]" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_moving_avg(data, k):\n", - " moving_avg = []\n", - " for i in range(len(data)):\n", - " if i+k > len(data):\n", - " break\n", - " # print(data[i:i+k])\n", - " moving_avg.append(sum(data[i:i+k])/k)\n", - " # print(chunk) \n", - " return moving_avg\n", - "\n", - "three_week_avg = calc_moving_avg(sales, 3)[:-1]\n", - "four_week_avg = calc_moving_avg(sales, 4)[:-1]\n", - "five_week_avg = calc_moving_avg(sales, 5)[:-1]\n", - "six_week_avg = calc_moving_avg(sales, 6)[:-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.222222222222221\n", - "9.6484375\n", - "7.405714285714287\n", - "6.791666666666667\n" - ] - } - ], - "source": [ - "three_mse = sum([(i-u)**2 for i, u in zip(sales[3:], three_week_avg)])/ len(three_week_avg)\n", - "four_mse = sum([(i-u)**2 for i, u in zip(sales[4:], four_week_avg)])/ len(four_week_avg)\n", - "five_mse = sum([(i-u)**2 for i, u in zip(sales[5:], five_week_avg)])/ len(five_week_avg)\n", - "six_mse = sum([(i-u)**2 for i, u in zip(sales[6:], six_week_avg)])/ len(six_week_avg)\n", - "\n", - "print(three_mse)\n", - "print(four_mse)\n", - "print(five_mse)\n", - "print(six_mse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exponential Smoothing" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "week= [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", - "sales= [17, 21, 19, 23, 18, 16, 20, 18, 22, 20, 15, 22]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$S_t = (a-1) S_{t-1} + f(a, S_{t-1})$
\n", - "$S_{t-1} = (a-1) S_{t-2} + f(a, S_{t-2})$
\n", - "$S_{t-2} = (a-1) S_{t-3} + f(a, S_{t-3})$
\n" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[17,\n", - " 19.8,\n", - " 19.240000000000002,\n", - " 21.872,\n", - " 19.1616,\n", - " 16.94848,\n", - " 19.084544,\n", - " 18.3253632,\n", - " 20.89760896,\n", - " 20.269282688,\n", - " 16.5807848064,\n", - " 20.37423544192]" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# calc_something = lambda x, a: x*a + \n", - "\n", - "\n", - "def calc_exponential_smoothing(data, alpha):\n", - " preds = [data[0]]\n", - " length = len(data)\n", - " for period in range(1, length):\n", - " preds.append(((1-alpha)*preds[period-1]) + alpha * data[period])\n", - "\n", - " return preds\n", - "calc_exponential_smoothing(sales, 0.7) \n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/ManufacturingCosts_Problem.ipynb b/Time Series/ManufacturingCosts_Problem.ipynb deleted file mode 100644 index b7dfac8..0000000 --- a/Time Series/ManufacturingCosts_Problem.ipynb +++ /dev/null @@ -1,189 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The president of a small manufacturing firm is concerned\n", - "about the continual increase in manufacturing costs over the past several years. The fol-\n", - "lowing figures provide a time series of the cost per unit for the firm’s leading product\n", - "over the past eight years:\n", - "\n", - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "\n", - "b. Use simple linear regression analysis to find the parameters for the line that mini-\n", - "mizes MSE for this time series.\n", - "\n", - "c. What is the average cost increase that the firm has been realizing per year?\n", - "\n", - "d. Compute an estimate of the cost/unit for the next year." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "year = [1,2,3,4,5,6,7,8]\n", - "data = [20.00,24.50,28.20,27.50,26.60,30.00,31.00,36.00]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.ylim(bottom = 0, top = max(data)*1.5)\n", - "plt.plot(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "b_0 = m.continuous_var(name = 'Intercept')\n", - "b_1 = m.continuous_var(name = 'Slope')\n", - "\n", - "mse = []\n", - "for i in range(len(year)):\n", - " mse.append((b_0 + b_1 * year[i] - data[i])**2)\n", - "\n", - "m.minimize(sum(mse))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.01 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 2\n", - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - "Tried aggregator 1 time.\n", - "QP Presolve added 0 rows and 2 columns.\n", - "Reduced QP has 2 rows, 4 columns, and 5 nonzeros.\n", - "Reduced QP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.05 sec. (0.00 ticks)\n", - "Parallel mode: using up to 4 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 4\n", - " Rows in Factor = 2\n", - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 3.8055000e+03 6.4163000e+03 2.39e+00 0.00e+00 6.08e+03\n", - " 1 2.1409229e+03 5.6826884e+03 7.75e-01 0.00e+00 1.97e+03\n", - " 2 4.7175047e+02 1.7525819e+03 1.35e-01 0.00e+00 3.42e+02\n", - " 3 9.9267661e+01 -5.9577420e+02 3.93e-02 0.00e+00 9.99e+01\n", - " 4 3.7922865e+01 -5.8277290e+02 0.00e+00 0.00e+00 1.29e-12\n", - " 5 2.3408803e+01 1.4755809e+01 3.55e-15 0.00e+00 3.20e-14\n", - " 6 2.3346193e+01 2.3296668e+01 3.46e-14 0.00e+00 4.66e-14\n", - " 7 2.3346191e+01 2.3321430e+01 1.87e-14 0.00e+00 4.50e-14\n", - " 8 2.3346191e+01 2.3333810e+01 5.33e-15 0.00e+00 8.36e-14\n", - " 9 2.3346190e+01 2.3340001e+01 4.44e-15 0.00e+00 8.64e-14\n", - " 10 2.3346190e+01 2.3343095e+01 8.88e-16 0.00e+00 3.42e-13\n", - " 11 2.3346190e+01 2.3344643e+01 7.99e-15 0.00e+00 1.73e-13\n", - " 12 2.3346190e+01 2.3345417e+01 3.55e-15 0.00e+00 1.72e-14\n", - " 13 2.3346190e+01 2.3345804e+01 3.55e-15 0.00e+00 6.01e-14\n", - " 14 2.3346190e+01 2.3345997e+01 0.00e+00 0.00e+00 7.00e-14\n", - " 15 2.3346190e+01 2.3346094e+01 0.00e+00 0.00e+00 2.79e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 16 2.3346190e+01 2.3346142e+01 1.78e-15 0.00e+00 2.22e-13\n", - "Barrier time = 0.11 sec. (0.02 ticks)\n", - "\n", - "Total time on 4 threads = 0.11 sec. (0.02 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model2\n", - "objective: 23.3462\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Intercept=19.993\n", - "Slope=1.774\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/SalesForcast_Problem.ipynb b/Time Series/SalesForcast_Problem.ipynb deleted file mode 100644 index ace7914..0000000 --- a/Time Series/SalesForcast_Problem.ipynb +++ /dev/null @@ -1,35 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following time series shows the sales of a particular product over\n", - "the past 12 months.\n", - "\n", - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "\n", - "b. Use a 5 0.3 to compute the exponential smoothing values for the time series.\n", - "\n", - "c. Use trial and error to find a value of the exponential smoothing coefficient a that\n", - "results in a relatively small MSE." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = [105, 135,120,105,90,120,145,140,100,80,100,110]" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/SeasonalEffects_Problem.ipynb b/Time Series/SeasonalEffects_Problem.ipynb deleted file mode 100644 index cc1cd7d..0000000 --- a/Time Series/SeasonalEffects_Problem.ipynb +++ /dev/null @@ -1,204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "a. Construct a time series plot. What type of pattern exists in the data? Is there an indi-\n", - "cation of a seasonal pattern?\n", - "\n", - "b. Use a multiple linear regression model with dummy variables as follows to develop\n", - "an equation to account for seasonal effects in the data: Qtr1 5 1 if quarter 1, 0 other-\n", - "wise; Qtr2 5 1 if quarter 2, 0 otherwise; Qtr3 5 1 if quarter 3, 0 otherwise.\n", - "\n", - "c. Compute the quarterly forecasts for the next year." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "year = [1, 2, 3]\n", - "quarter = [1, 2, 3, 4] \n", - "data = [[71, 68, 62],\n", - " [49, 41, 51],\n", - " [58, 60, 53],\n", - " [78, 81, 72],]\n", - "\n", - "flatten_data = lambda x: [data[i][idx] for idx in range(len(data[0])) for i in range(len(data)) ]\n", - "\n", - "data_flat = flatten_data(data)\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.ylim(bottom = 0,top = max(data_flat)*1.1)\n", - "plt.plot(data_flat)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def create_dummies(data, quarter):\n", - " data = np.array(data).T.flatten()\n", - " dummies = np.zeros((len(data), len(quarter)))\n", - "\n", - " for i in range(len(dummies)):\n", - " dummies[i][i%(len(quarter))] = 1\n", - "\n", - " dummies = dummies[:, :-1]\n", - " \n", - " return dummies\n", - "\n", - "\n", - "data_dummied = create_dummies(data, quarter)" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "b_0 = m.continuous_var(name = 'Intercept', lb = -10e6)\n", - "b_x = m.continuous_var_list(range(len(quarter) - 1), name = 'Slope', lb = -10e6)\n", - "\n", - "mse = []\n", - "for i in range(len(data_dummied)):\n", - " mse.append((b_0 + b_x @ data_dummied[i] - data_flat[i])**2)\n", - "\n", - "m.minimize(sum(mse))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 3\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 4\n", - " Integer space required = 4\n", - " Total non-zeros in factor = 10\n", - " Total FP ops to factor = 30\n", - "Tried aggregator 1 time.\n", - "QP Presolve added 0 rows and 4 columns.\n", - "Reduced QP has 4 rows, 8 columns, and 11 nonzeros.\n", - "Reduced QP objective Q matrix has 4 nonzeros.\n", - "Presolve time = 0.03 sec. (0.00 ticks)\n", - "Parallel mode: using up to 4 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 4\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 4\n", - " Rows in Factor = 4\n", - " Integer space required = 4\n", - " Total non-zeros in factor = 10\n", - " Total FP ops to factor = 30\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 2.0918266e+13 -2.0962679e+13 1.51e-08 0.00e+00 1.15e+07\n", - " 1 2.3719024e+04 -5.1739053e+09 1.64e-08 0.00e+00 4.43e-10\n", - " 2 1.6658884e+02 -2.5869245e+07 8.92e-08 0.00e+00 2.49e-12\n", - " 3 1.6600003e+02 -1.2918105e+05 7.37e-08 0.00e+00 4.32e-13\n", - " 4 1.6600000e+02 -4.8073526e+02 6.94e-08 0.00e+00 3.80e-13\n", - " 5 1.6600002e+02 1.6276632e+02 1.21e-07 0.00e+00 1.31e-13\n", - " 6 1.6599999e+02 1.6519158e+02 5.37e-08 0.00e+00 6.59e-14\n", - " 7 1.6600001e+02 1.6599596e+02 4.04e-08 0.00e+00 2.58e-13\n", - " 8 1.6600000e+02 1.6599899e+02 1.33e-08 0.00e+00 3.26e-13\n", - " 9 1.6600000e+02 1.6599975e+02 1.33e-08 0.00e+00 1.71e-13\n", - "Barrier time = 0.07 sec. (0.02 ticks)\n", - "\n", - "Total time on 4 threads = 0.07 sec. (0.02 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model28\n", - "objective: 166\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Intercept=77.000\n", - "Slope_0=-10.000\n", - "Slope_1=-30.000\n", - "Slope_2=-20.000\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/Smartphone_Problem.ipynb b/Time Series/Smartphone_Problem.ipynb deleted file mode 100644 index bec9f31..0000000 --- a/Time Series/Smartphone_Problem.ipynb +++ /dev/null @@ -1,192 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "Year = [1,2,3,4]\n", - "Quarter = [1, 2, 3, 4]\n", - "period = range(1, 17)\n", - "data = [4.8,4.1,6.0,6.5,5.8,5.2,6.8,7.4,6.0,5.6,7.5,7.8,6.3,5.9,8.0,8.4]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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D/ju4papXLN6ELDI0oGSvjykLk7TtcKaVJXuE3WnZmntu1KO0L+QV4Out8XfGS5JmrTmotftPWlxR6fHesuIw3r9ltCqUYyn1SyF8AJBUPFz89YZD6vLmEv0v8ZBsNmlQm1gtfLyTejWp+ruVM/s0i1KPxpEqchh6/MvNyi+yX+KRcSUmL9wjw5B6xkeqQdXSO+pxXpua4RpwY7Qkaew3W/j9S9pxJEvLk46zlPoVIHwA0L5jZ/Tn99foH19t1qncQtWPDNbXD7XTv/o2VsglpnnabDa91LexKpX30+70bE2cv8fkqj3HzqNZmrs1TTabNLJL6ez1uJixPRqoUnl/7T2Wo6mLufz2n3MbyPWMr6rosHIWV1O6ET6AMiy/yK4pC5N02+TlSth3QgG+Xnrqtvr6/rEOV7SDaXh5f42/s4mk4j0s1h9g+P1aTF6QJKn4TatepPssXR5azlcv9G4kSZq2JFl70rMtrsg6R06f1ZzNxVOkWVTs8ggfQBm1dv9J9ZqyQhPn71FBkUMd61TSL6M66eGba13Vbqi3NozQ3S2qyzCkx7/arJz8IhdW7Xm2H8nUvO3nRz1Kd6/HxfSMj1TXBhEqtBsa878tcvxql92y5KNVB1TkMNSmZpiaVK9gdTmlHuEDKGNO5xZozP+2qP97CUrOOKNK5f00eUAzzRjaSjHh1zZU/OztDRUVGqCUE7ka/9NOJ1fs2c6PevSKr6q6Ee4z6nGezWbTv/o2Unl/HyUePK1P16RYXZLpsvIKNWtN8VLqD95Uy+Jq3APhAygjDMPQd5sOq+vEpfp8Xaok6d5W0VowupP6NKt2XVuxhwT46vV+TSVJn64+qGV7jjmlZk+37XCmftmR7rajHudVDQ3Uk7cVL73+2rzdOnL6rMUVmeuzNQd1Jr9IdaqUV6e6la0uxy0QPoAyIOVEju7/YK1Gfr5Jx88UqHaV8vrywbYaf2cTp00HbF+7kga3LV7X4Mmvtygzl8WnLmfywuJRj9ubRKmOG456/Np9rWN1Q0wFnckv0rPfbZNhlI3LLwVFDn248oAkadhNLKV+pQgfgAcrtDv07yXJ6vbWMi1POi4/Hy89fmtdzR3RUa3iwpx+vjE9GiiuUpDSsvL0/Pfbnf74nmTb4UzN35EuL5s0wo1HPc7z8rLp1buayNfbpgU7MzR3a9lYev37zUeUlpWnKsH+6tMsyupy3AbhA/BQG1JO6U9TVui1ebuVX+RQu1rhmjeyox7rUkd+Pq75rx/o5603+zeVl02avfGw5m076pLzeIJJC4qnJvduGqXaVcpbXI1z1I0I1sM315YkPTdnu8ePfhmGoffPTa/9S/sa8vdhKfUrRfgAPEzm2UI9M3ur7n53lXanZ6tiOV+92a+pZv6ttWpWdv2b3A0xFfVQp+Kmu6dnb9PxM/kuP6e72XLotBbszPCYUY9fG965lmpVDtLxM/ke33y8LOm4dqVlq5yftwa2Yin1q0H4ADyEYRj6YcsRdZ24VDPXHJRhSHe3qK6Fj9+su1pUv66G0qs1smsd1Y8M1smcAj39zdYyc/3/Sk06N8Olb7NqpgRCM/n7eOvVu4rXfvl8XaoS9p6wuCLXef/cBnIDboxRaLmLL8aHiyN8AB4g9WSuhn60To/O2qhj2fmqWSlInw1rozf6NVVYkPn7S/j7eOute5rJ19umX3ak65vEw6bXUFptSj2tRbuKRz0evaW21eW4xI01wjSwdYwk6enZW5VX6HlLr287nKkVycfl7WXT0A41rC7H7RA+ADdWZHdo+rK96vbWMi3efUx+3l4a2aWO5o7sqLa1wi2trUHVkJJt4Z+fs73MTb+8lMnnej36Nve8UY9fe6pHfUWE+Gv/8Ry9vSjJ6nKc7nyvR6/4qiUbLuLKET4AN7UrLUu931mpV+bu0tlCu1rFhWnuyI76+611FeBbOhrfHryppprHVFB2fpGe/Lrsrn553saDp7R49zF5e9k04hbP6vX4rZAAX73Qu3jn4/eW7tPOo1kWV+Q8h0+f1Q9bipupWUr92hA+ADeUW1CkoR+u046jWQoN9NVrdzXR58PalLpZEz7eXnqzX1MF+HppRfLxMrn65a+d7/W4o3k11agUZHE1rndb40h1bxShIkfx0ut2DwmfH6zYL7vDULta4WpcLdTqctwS4QNwQ1MXJ+tIZp6qVQjUwsc7qf+N0aV2caOalctrzG31JUmvzN2p/cdzLK7IGhtSTmnpnuJRj8c8tNfjYl7s01jB/j7afChTH686YHU51y3zbKE+X1u8lDqjHteO8AG4mf3Hc/T+sv2SivdUqVTe3+KKLu/+tjXUrla48godevzLTR7zCfhqnF/X464bqik23PNHPc6LCAnQmJ7F4fONX3br0Klciyu6PrPWHFROgV31IoJZSv06ED4AN2IYhp6fs10Fdoc61a2sbg0jrC7pinh52fR6v6YKPrf52HvL9lpdkqk2pJzU8qTj8vGy6TEP7/W4mHtvjFGrGmHKLbBr3Lfuu/R6fpFdH64sDv7Dbqpp6vR1T0P4ANzILzvStXRP8ayW53s3cqsXv2oVAvXs7Q0lSW/N36NdaZ7TgHg5b80v7vW4u0V1RYeVvZkRXl42vXJnvPy8vbR49zHN2XzE6pKuyZxNR5SRna+IEH/1bspS6teD8AG4ibMFdr34/Q5J0rCb4hTnhg2Ld7eorq4NIlRoN/T3LzaroMhhdUkut+7ASa1ILh71GN657PR6/FbtKuVL1jV58fsdOpVTYHFFV+fXS6kPaR/nsi0Kygr+9QA38e8lyTp8+qyiQgPc9k3MZrNp/J3xqljOVzuPZmnKQs9b/+G3zvd69GtZNkc9fu2hTrVUN6K8TuQU6OW57rX0+pI9x7Qn/YzK+/voz+cWUMO1I3wAbuDA8Ry9t7T4U9e4PzVUOT8fiyu6dpWD/fXyHfGSigPVxoOnLK7IddbuP6mVySfk6122Rz3O8/Px0vg7m8hmk77ecEgrko5bXdIVm37u/9+9raIVEsBS6teL8AGnMwyjzC8m5UyGYeiF74ubTDvWqaTbGkdaXdJ16xlfVX2aRclhSI9/uVlnCzxv+W2puLdFkvq1jGYVzHNaxFbUoDbFm7A9PXurW/zutx7KVMK+E/LxsmlI+ziry/EIhA84jd1h6JvEQ+r8xhK1e3WRW32qKc0W7MzQ4t3H5Ottc7sm0z/yYu/Gigjx177jOZowb5fV5Tjd6n0nlLCPUY+LeaJ7PVUNDdDBk7matHCP1eVc1vRzvR63N41SVIVAi6vxDIQPXDfDMDRvW5pum7RMo7/crAMncpWWladBH6zRhHm7VGj3/KZCV8krtOuF77dLkv7aoaZqedBeIKHlfDXh3O6nH606oFXJnhVWz4963HNjtKrxhnWB4ABf/atP8dLr/1m+X9sOZ1pc0aWlnszV3K3FS6kP68iiYs5C+MA1MwxDy5OOqc/UlXro0w1Kyjij0EBfPXlbPQ1sHSPDkKYt2av+7yUo9aR7LyxklWlL9urQqbOqGhrgkati3lyvSknz3hNfb1FWXqHFFTnHqr3HtWb/Sfl5ezHqcQldG0aoV3xV2R2Gxn6zVUWl9EPKByuLl1LvWKeSGkaFWF2OxyB84JpsSDmpe99frUH/XasthzJVzs9bj91SW8ue7KxHbq6tl++I178H3qDgAB9tPHhaPacs14/nNmLClTl4IlfTlhYvxvVMrwYK8nffJtM/8kzPBooJK6fDp8/qX+emErszwzBK9nAZ0CpaVUMZ9biU53o3VEiAj7YeztSHKw9YXc7vZOYW6ot1qZJYSt3ZCB+4KtuPZGroR+t017QErd53Un4+Xvprhzgte7KzHu9WT6GB/98F3jO+quaO6KgbYiooO69Iw2clauw37tFgVhq8+MN2FRQ51K5WuHrFV7W6HJcJ8vfRG/2aymaTvtpwSAt2pFtd0nVJ2HtCa8+Nejx8cy2ryynVqgQH6JleDSRJE+fvKXUjpJ+uSVFugV0NqoaoQ+1KVpfjUQgfuCL7jp3Ro7MS1WvKCi3alSFvL5vubRWtJf+4WeP+dOn9RaLDyumLB9tqeOdastmkz9YeVJ+pK7Q7LdvkZ+BeFu1K14KdGfLxsunFPp7TZHopreLCSq6nj/lmq0662QJU5xmGobfOretxL6MeV6R/y2i1qRmms4V2PT17a6lZer14KfUDkqQHborz+P+DZiN84A8dPn1WT329Rbe+tUw/nLts0rtplBaM7qTxdza5os5vX28vPdG9vj79a2tVDvbXnvQz6v3OCs1ck1JqXmhKk7xCu56fU3z5YWiHONWuEmxxReYYfWtd1alSXsfP5Lvt/h8rk09o3YFT8vPx0iP0elyR4oXnmsjPx0vLk47rlbk7lZGdZ3VZ+nbjYR0/k6+qoQH6UxOWUnc2wgcu6lh2vp6fs12dX1+iL9anyu4w1LVBhH4a2VFT7m1+TUt7t69dST+N7Kib61VWfpFDz8zepkdmJirzrGc0GTrL9GX7dPBkriJC/DWiS9nZhCzA11sT+zeTj5dNP2496nb7f/x61OPPrWIUERJgcUXuI65SkEZ1Lf5bf3/5frV/dZEe+2yj1h04aUkIdTgMvb+8eAO5oe3j5OvNW6WzeWYHG65ZZm6hpi/fqw9WHNDZwuLejHa1wvWP7vV0Q0zF6378SuX99cHgG/XfFfv12s+79NO2NG05lKkp9zZXi9jrf3x3l3oyV1MXJ0uSnu7ZQOU9tMn0UuKrh+rRW2pr0oIkPfvddrWpGe42b+LLk45rQ8op+ft46RF6Pa7aw51qqVqFQH286oASD57W95uP6PvNR9SgaojubxurPs2iTFvZd/HuDCVnnFGwv48GtIo25Zxljc0oZWObWVlZCg0NVWZmpkJCmNZkltyCIn248oDeW7pXWXlFkqRm0RX0RPd6au+iRqvNqaf12GcbdfBkrry9bBp9a1091KmWvL3K7rXVB2as1y870tU6LkyfP9CmTF5nLrQ7dOe/V2nr4Ux1qltZHw25sdT/OxiGoTunrdLGg6c1tH1cye69uDbbDmfqk4QUfbf5sPIKi6fgBgf4qF+LaA1qG+vyTRXveS9Ba/af1IM31dTYng1cei5PcjXv34SPMi6/yK5Zaw5q6uJkHT9T3ORXLyJY/+heT10bVHH5i352XqH++e02fbepeIi9fe1wvdW/maq4yaddZ1qyO0N/+XCdvL1s+mlkR9WNKBu9HheTlJ6tXm+vUEGRQ6/cEV/qN/JauueYBn+wVgG+Xlr2ZGdVCS57f7+ucDq3QF+tP6RP16Qo5cT/z4TpWKeS7m9bQ7fUr+L0DyubU0+rz9SV8vGyacVTtygylN/llSJ84LKK7A59k3hYkxcm6fDps5Kk2PByGn1rXf2pSZSpow+GYejrDYf07HfbdbbQrvAgP73Rv6k616tiWg1Wyy+yq/tby3TgRK7+1iFO//wTn5z/s3yfXvpxp8r5eWveyJsUE14690YxDEN3/HuVNqWe5nfnIg6HoaVJx/RJQooW787Q+XetahUCNbBNjO5pGa3wS8y4u1rDZybqx61HdecN1TSxfzOnPGZZQfjAJTkchn7celRvzd+jfcdzJEmRIQEa0aWO+rWsbmljVXLGGT322UbtPJolSRrWMU5PdK8vPx/Pb/aaujhZr/+8W5WD/bXo8U4KZtdMORyGBry/Wmv3n1SrGmH67IE2pfKS3OLdGRry4ToF+Hpp+ZO3qHKwc94EcXEHT+Rq5poUfbE+Vadzi5vV/Xy89Kf4qrq/XQ01i65wXY998xuL5TCkeaM6qn4k70FX42rev13yqn748GHdd999Cg8PV2BgoOLj47V+/XpXnApXyDAMLdqVrl5vr9Bjn23UvuM5Cgvy0z97NdCSJ27Wn1vHWN7RXbtKec1+pJ3+0q6GpOKu97vfXaUD50KSpzp8+qzeXlS8IuYzPRsQPM7x8rLpzX5NFeTnrbUHTuqDFfutLul3DMPQpHN7uAxqE0vwMEFMeDmN7dlAq8d20et3N1GT6qEqKHLom42H1XfqSvV+Z4W+Wp+qvMKrX8zwvyv2yWFInepWJni4mNNHPk6dOqXmzZurc+fOevjhh1W5cmUlJSWpVq1aqlXr8h3gjHw43+p9J/T6z7u1IeWUJCnY30fDbqqpoR3iSu1sil+2p+nJ/23R6dxClff30ct3NFafZtWsLsslHv50g37alqZWcWH6oow2mf6Rz9Ye1NhvtsrPx0s/PNahVPXCLNqVrqEfrVegr7eWP9X5kovtwbU2pZ7WjIQD+mHLURUUFTeoVijnq3taRuu+NrGKDrv8JbtTOQVq9+oinS20a+bfWrus0d6TWXrZZcyYMVq5cqWWL19+TT9P+HCeHUeyNP6nnVp+bmv7AF8vDW5XQw/dVEsVg/wsru7yjpw+q1Gfb9LaAyclSXe3qK4XejfyqD1Olicd06D/rpW3l00/jujAp62LMAxDQz5apyW7j6lxtRB9/VA7Bfh6W12WDMNQn6krteVQJrMiSomTOQX6Yl2qPl2dUtLLZrNJnetV0aC2sepUp7K8LnHp7u2FSXpz/h41igrRD4914EPANbA0fDRs2FDdu3fXoUOHtHTpUlWrVk2PPPKIhg0bdtH75+fnKz8//4Lio6OjCR/XKSM7T51fX6KcArt8vW26t1WMHu1c2+1mkRTZHXp7UbLeXpQkhyHVrBykt+9trkZRoVaXdt0Kihy6bfIy7TuWoyHta+i52xtZXVKplZ6Vp25vLVPm2cKr/kTrKgt2pOtvM9arnJ+3lj/Z2WkNj7h+doehxbsyNGN1ipbtOVZyPDa8nO5rHat+LaurQrn//wCWV2hXhwmLdPxMgSYPaOaxo6yuZmn4CAgofnMbPXq0+vXrp3Xr1mnkyJF69913NXjw4N/d//nnn9cLL7zwu+OEj+vz4vc79MHK/WpQNUTTB7Ww9EXaGVbvO6FRn29SWlae/Ly99EyvBrq/baxbfzqZtmSvJszbpUrl/bXoH50UQq/HH1qedExj/rf1qj/RuoJhGLr9nRXadjhLD3WqpTE96pt2blydfcfO6NPVB/XVhlRln1vDKMDXS72bRun+tjXUuFpoyaW9ahUCteSJmy3vf3NXloYPPz8/tWzZUqtWrSo5NmLECK1bt04JCQm/uz8jH86XkZWnjq8tVn6RQx8PbaVOdStbXZJTnMwp0JNfb9aCnRmSpFsbRui1u5q4xSWk3zqaeVZd3lyq3AK73uzXVHe1qG51SW7B7jC0aFeGZiQcKLmcKF36E62rzN+RrmEz1ivIz1vLn7pFYW74N1jW5BYU6btNRzQjIaVkRp0k3RBTQelZ+Tp8+qzG/amh/tohzsIq3Zuls12qVq2qhg0vnOfeoEEDHTx48KL39/f3V0hIyAVfuD7Tlu5VfpFDN8RU0E11PKdpKizIT+/f31LP3d5Qft5emr8jXT2nLNeafSesLu2qvfTjTuUW2NUytqLuvIEh3ivl7WXTrQ0j9MlfW2vR4500tH2cggN8lHIiVy/P3ak24xfqya83a9vhTJfVYBiGJp3bw2VwuxoEDzdRzs9H97aK0dwRHfT1Q23Vu2mUfL1tSjx4WodPn1VwgI/uuZGl1M3i9M699u3ba/fu3Rcc27Nnj2JjY519KlxEelaeZq4pDnp/v7WuW1+WuBibzaYh7eN0Y40wjTg3Zfje91drRJc6euyWOqVyHYjfWpl8XD9uOSovm/Rin8Ye9zsyS83K5fXs7Q31j+519e3GI5qRcEC70rL15fpD+nL9Id0QU0H3t62hHvGR8vdxXoPqLzvStf1IloL8vDWsY02nPS7MYbPZ1LJGmFrWCNOx7Ib6fO1B/bIjXYPaxJba2X+eyOmXXdatW6d27drphRdeUP/+/bV27VoNGzZM06dP18CBAy/788x2uT7Pz9muj1YdUMvYivrqobYe/caWk1+k5+Zs19cbDkmSWsWFafKAZqoaGmhxZZdWUORQzynLlZxxRoPbxuqFPo2tLsljGIah9SmnNCMhRT9tPaoiR/FLW3iQnwa0itafW8eqWoXr+9twOAz1enuFdh7N0vDOtfREd3o9gPMsX+H0hx9+0NixY5WUlKS4uDiNHj36krNdfovwce3SMvN00+uLVVDkKFPz1L/deFjPzN6qnAK7QgN99UzPBurXsnqpDF7Tl+3VK3N3KTzIT4v+cbNCA2kydYWM7Dx9vjZVs9YcVFpWniTJyyZ1bRCh+9vWUPva4df09zFv21E99Gmiyvv7aMVTnU3pLwHcheXh43oQPq7ds99t04yEFLWqEaYvHixbi1XtP56jEZ9t1NZz1/pbxYXplTviVbtKeYsr+3/pWXm65Y3i6c+v3d1E/VtyfdnViuwOzd+RrhkJKUr4VW9QzcpBGtQmVne1qH7Fs4wcDkM9pyzXrrRsPXZLbT3erZ6rygbcEuGjDDpy+qxufn2JCuwOzRrWWu1qlY1Rj18rsjv04coDmjh/j84W2uXn7aWHb66lh2+uVSoWpRrx2UbN2XxEzWMq6H8PtTN1aiiKd8r9ZHWK/rfhkHIKipfeLufnrb7Nq+n+trGXXeDtp61H9fDMRAX7+2jFU7cotByjVsCvWb63C8w3bcleFdgdah0XViaDhyT5eHtp2E019cvfb1LnepVVYHdo8sIk9Zy8XAl7rZ0Rk7D3hOZsPiKbTfpXn8YEDwvUiQjWi30aa80zXfWvPo1Up0p55RbYNWvNQd02abn6v5ug7zcfUaHd8bufdTgMTVpQvP/OkA5xBA/gOjHy4QF+Perx2bA2alsr3OqSLGcYhuZuTdPz32/XsezidWT6taiup3s2MH1dkEK7Q72mLNee9DO6r02MXuobb+r5cXGGYWj1vpP6ZPUB/bw9XfZzDaqVg/11b6sYDWwdo4hzKwL/uOWohs9KVHDAuVEPenWA37ma92/mFXmAqYuTVWB3qE3NMILHOTabTb2aVFWHOpX0+s+7NHPNQX214ZAW7srQP3s10B3Nq5nWE/PxqgPak35GFcv56h/0CZQaNptNbWuFq22tcB3NPKvP1hzUrLWpOpadrykLkzR1cbK6N4rQoDY1NHlh8boef+0QR/AAnICRDzd36FSuOr+xRIV2Q1880EataxI+LmZDyik9/c1W7U7PliS1rx2ul/rGK65SkEvPm5GVp1veXKoz+UV69c54DWgV49Lz4foUFDk0b3uaPkk4oHUHTl1wG6MewB+j56MMmbp4rwrthtrVCid4/IEWsRX1w4gOevK2evL38dLK5BPqPmmZ3lmUVLIFtyu8MnenzuQXqWl0BWa3uAE/n+I9P756qJ3mjuioe1vFKPBcs/JDnWoRPAAnYeTDjaWeLB71KHIY+uqhtrqxRpjVJbmFlBM5+ue320r2BqldpbzG3xnv9H+/NftO6J7pq2WzSd8Nb68m1Ss49fFhjsyzhUpKz1aL2Iplavo6cLUY+Sgjpi5OVpHDUIfalQgeVyE2PEgzhrbS5AHNFB7kp+SMM+r3boLGfrNFmbmFTjlHkd2h5+ZslyQNuDGG4OHGQgN91bJGGMEDcCLCh5tKPZlbsqz4qK51LK7G/dhsNvVpVk0LH++kAec2k/psbaq6TFyi7zYd1vUOCM5ISNGutGxVKOerJ7vTZAoAv0b4cFPvLCoe9ehYp5JaMupxzSqU89OrdzXRFw+0Ua3KQTp+pkAjP9+kwR+uU+rJ3Gt6zIzsPL01v3h2xJPd65s+tRcASjvChxs6eCJXXyeeH/Woa3E1nqF1zXDNHdlRo2+tKz8fLy3bc0y3vrVU05bsveiiU3/k1Z92KTu/SE2qh7JFNwBcBOHDDb29KEl2h6Gb6lZWi9iKVpfjMfx9vDWiSx3NG9lRbWuGK6/QoQnzdun2t1co8eCpyz+ApPUHTuqbxMOy2aQX+zSWNyuZAsDvED7czIHjOfpm42FJ0t/p9XCJmpXLa9aw1nqjX1NVLOerXWnZumvaKo37dpuy8i7dkFpkd2jcd8VNpve0jFaz6AomVQwA7oXw4WbeXpQsu8PQzfUqq3kMox6uYrPZdHeL6lr4+M2664bqMgzpk9Up6vrmUs3devSiDakz1xzUzqNZCg301ZO31begagBwD4QPN7L/eI5mb6TXw0xhQX56s39TzRrWWnGVgpSRna9HZibqbx+v1+HTZ0vud/xMvt74Zbck6R/d6ymMJlMAuCTChxt5e2GSHIZ0S/0qDOmbrF2tSvppZEeNuKW2fL1tWrgrQ7dOXKr/LN+nIrtDE37apey8IjWuFqI/s4Q6APwhVjh1E3uPndGtE5fKYUhzHmW1TCslZ2Tr6W+2ae2Bk5KkOlXKKynjjCTpfw+3owkYQJnECqce6J1FyXIYUpf6VQgeFqtdJVifP9BGr94Zr5AAn5Lg0a9FdYIHAFwBH6sLwOXtPXZG320qnuFCr0fp4OVl04BWMerSIEKv/7xLR07naUwPmkwB4EoQPtzAlHO9Hl0bRCi+eqjV5eBXKgf767W7m1pdBgC4FS67lHLJGdmas/mIJPZwAQB4BsJHKTd5YbIMQ+rWMEKNqzHqAQBwf4SPUmxPerZ+2HJ+1INeDwCAZyB8lGKTFybJMKTbGkWqYRTTjgEAnoHwUUrtTsvW3K1HJUkj6fUAAHgQwkcpNeXcqEePxpFqUJVRDwCA5yB8lEK70rL0I6MeAAAPRfgohSYvSJIk9YqvqvqRjHoAADxLmQofpWwbm4vacSRLP21Lk83GqAcAwDOVmfDhcBgaNmO9Ply5v1SHkMkL90gqHvWoGxFscTUAADhfmQkf87anacHODL3w/Q4Nm7Fep3IKrC7pd7YfydTP29OLRz26MOoBAPBMZSZ89GgcqRd6N5Kft5cW7MxQj8nLtXrfCavLusCkc70etzeJUh1GPQAAHqrMhA+bzabB7Wpo9vB2qlk5SGlZefrz+6v11vw9KrI7rC5P2w5nav6O4lGPEV1qW10OAAAuU2bCx3mNokL1/aMddHeL6nIYxauI/vk/a3Q086yldZ0f9ejdNEq1qzDqAQDwXGUufEhSkL+P3ujXVJMHNFN5fx+t3X9SPSYv1/wd6ZbUs/VQphbsTJeXTRpBrwcAwMOVyfBxXp9m1fTjiA5qUj1Up3MLNWzGej0/Z7vyCu2m1jFpwZ6SempVLm/quQEAMFuZDh+SFBsepK8faqdhHeMkSR+tOqA7/r1Ke4+dMeX8m1NPa+GuDHnZpMduodcDAOD5ynz4kCQ/Hy8906uhPhxyo8KD/LTzaJb+NGWFvlqf6vI1Qc6PevRtXk01GfUAAJQBhI9f6Vyvin4a2VHta4frbKFdT3y9RaO+2KTsvEKXnG/jwVNavPuYvL1sGnELvR4AgLKB8PEbVUICNGNoaz3RvZ68vWz6btMR/entFdpy6LTTz3V+hkvfZtVUo1KQ0x8fAIDSiPBxEd5eNg3vXFtfPthG1SoEKuVEru6atkrvL9snh8M5l2ESD57S0j3nRj1Y1wMAUIYQPv5Ai9gwzR3ZUT0aR6rQbujluTs19ON1On4m/7of+/yox53Nqyk2nFEPAEDZQfi4jNBAX/174A16+Y7G8vfx0pLdx9Rj8nKtTD5+zY+5IeWUlu05Jh8vmx6j1wMAUMYQPq6AzWbTwNaxmvNoB9WpUl7HsvN133/X6PWfd6nwGpZmPz/D5a4bqismvJyzywUAoFQjfFyFepHBmvNoB93bKkaGIU1dvFf3vJeg1JO5V/wY6w+c1PKk4/LxsulR1vUAAJRBhI+rFOjnrfF3xmvqn29QcICPEg+eVs8py/XT1qNX9PNvnRv16NeyuqLDGPUAAJQ9hI9r1KtJVc0d0VHNYyooO69ID89M1NOzt/7h0uxr95/UyuQT8jk3mwYAgLKI8HEdosPK6csH2+qRm2vJZpNmrTmoPu+s1J707Ive/63550c9olW9IqMeAICyifBxnXy9vfTkbfX1ydDWqhzsr93p2er9zgrNWnPwgqXZV+87oYR9J+TrTa8HAKBsI3w4SYc6lfTTyI7qVLey8godenr2Vj06a6MyzxYvzX5+hkv/ltGqViHQylIBALAU4cOJKpX314d/uVFP96wvHy+bftx6VL2mLNf7y/Zp9b6T8vP2otcDAFDmuTx8vPrqq7LZbBo1apSrT1UqeHnZ9MBNtfT1w+0UE1ZOh06d1ctzd0qS7rkxWlGMegAAyjiXho9169bpvffeU5MmTVx5mlKpWXQF/Tiig3o3jZIk+fl46ZHOtSyuCgAA67ksfJw5c0YDBw7U+++/r4oVK7rqNKVacICvJg9opk//2lr/e6idqoYy6gEAgMvCx/Dhw9WrVy917drVVadwCzabTR3qVFJ89VCrSwEAoFTwccWDfv7550pMTNS6desue9/8/Hzl5///LrFZWVmuKAkAAJQSTh/5SE1N1ciRIzVz5kwFBARc9v7jx49XaGhoyVd0dLSzSwIAAKWIzfj1SlhO8O233+qOO+6Qt7d3yTG73S6bzSYvLy/l5+dfcNvFRj6io6OVmZmpkJAQZ5YGAABcJCsrS6GhoVf0/u30yy5dunTR1q1bLzg2ZMgQ1a9fX0899dQFwUOS/P395e/v7+wyAABAKeX08BEcHKzGjRtfcCwoKEjh4eG/Ow4AAMoeVjgFAACmcslsl99asmSJGacBAABugJEPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABM5fTwMX78eN14440KDg5WlSpV1LdvX+3evdvZpwEAAG7K6eFj6dKlGj58uFavXq358+ersLBQ3bp1U05OjrNPBQAA3JDNMAzDlSc4duyYqlSpoqVLl+qmm2667P2zsrIUGhqqzMxMhYSEuLI0AADgJFfz/u3j6mIyMzMlSWFhYRe9PT8/X/n5+SXfZ2VlubokAABgIZc2nDocDo0aNUrt27dX48aNL3qf8ePHKzQ0tOQrOjralSUBAACLufSyy8MPP6yffvpJK1asUPXq1S96n4uNfERHR3PZBQAAN1IqLrs8+uij+uGHH7Rs2bJLBg9J8vf3l7+/v6vKAAAApYzTw4dhGHrsscc0e/ZsLVmyRHFxcc4+BQAAcGNODx/Dhw/XrFmz9N133yk4OFhpaWmSpNDQUAUGBjr7dAAAwM04vefDZrNd9PiHH36ov/zlL5f9eabaAgDgfizt+XDxsiEAAMDNsbcLAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmInwAAABTET4AAICpCB8AAMBUhA8AAGAqwgcAADAV4QMAAJiK8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAUxE+AACAqQgfAADAVIQPAABgKsIHAAAwFeEDAACYivABAABMRfgAAACmcln4mDp1qmrUqKGAgAC1bt1aa9euddWpAACAG3FJ+Pjiiy80evRoPffcc0pMTFTTpk3VvXt3ZWRkuOJ0AADAjbgkfEycOFHDhg3TkCFD1LBhQ7377rsqV66cPvjgA1ecDgAAuBEfZz9gQUGBNmzYoLFjx5Yc8/LyUteuXZWQkPC7++fn5ys/P7/k+8zMTElSVlaWs0sDAAAucv592zCMy97X6eHj+PHjstvtioiIuOB4RESEdu3a9bv7jx8/Xi+88MLvjkdHRzu7NAAA4GLZ2dkKDQ39w/s4PXxcrbFjx2r06NEl3zscDp08eVLh4eGy2WxOPVdWVpaio6OVmpqqkJAQpz52acTz9Ww8X89W1p6vVPaes6c9X8MwlJ2draioqMve1+nho1KlSvL29lZ6evoFx9PT0xUZGfm7+/v7+8vf3/+CYxUqVHB2WRcICQnxiF/0leL5ejaer2cra89XKnvP2ZOe7+VGPM5zesOpn5+fWrRooYULF5YcczgcWrhwodq2bevs0wEAADfjkssuo0eP1uDBg9WyZUu1atVKkyZNUk5OjoYMGeKK0wEAADfikvBxzz336NixY3r22WeVlpamZs2aad68eb9rQjWbv7+/nnvuud9d5vFUPF/PxvP1bGXt+Upl7zmXtef7azbjSubEAAAAOAl7uwAAAFMRPgAAgKkIHwAAwFSEDwAAYKoyEz6mTp2qGjVqKCAgQK1bt9batWutLsllxo8frxtvvFHBwcGqUqWK+vbtq927d1tdlileffVV2Ww2jRo1yupSXOrw4cO67777FB4ersDAQMXHx2v9+vVWl+USdrtd48aNU1xcnAIDA1WrVi3961//uqL9I9zBsmXLdPvttysqKko2m03ffvvtBbcbhqFnn31WVatWVWBgoLp27aqkpCRrinWCP3q+hYWFeuqppxQfH6+goCBFRUXp/vvv15EjR6wr+Dpd7vf7aw899JBsNpsmTZpkWn1WKRPh44svvtDo0aP13HPPKTExUU2bNlX37t2VkZFhdWkusXTpUg0fPlyrV6/W/PnzVVhYqG7duiknJ8fq0lxq3bp1eu+999SkSROrS3GpU6dOqX379vL19dVPP/2kHTt26M0331TFihWtLs0lJkyYoGnTpumdd97Rzp07NWHCBL322mt6++23rS7NKXJyctS0aVNNnTr1ore/9tprmjJlit59912tWbNGQUFB6t69u/Ly8kyu1Dn+6Pnm5uYqMTFR48aNU2Jior755hvt3r1bvXv3tqBS57jc7/e82bNna/Xq1Ve0NLlHMMqAVq1aGcOHDy/53m63G1FRUcb48eMtrMo8GRkZhiRj6dKlVpfiMtnZ2UadOnWM+fPnG506dTJGjhxpdUku89RTTxkdOnSwugzT9OrVyxg6dOgFx+68805j4MCBFlXkOpKM2bNnl3zvcDiMyMhI4/XXXy85dvr0acPf39/47LPPLKjQuX77fC9m7dq1hiQjJSXFnKJc6FLP99ChQ0a1atWMbdu2GbGxscZbb71lem1m8/iRj4KCAm3YsEFdu3YtOebl5aWuXbsqISHBwsrMk5mZKUkKCwuzuBLXGT58uHr16nXB79lTzZkzRy1btlS/fv1UpUoVNW/eXO+//77VZblMu3bttHDhQu3Zs0eStHnzZq1YsUI9evSwuDLX279/v9LS0i74uw4NDVXr1q3L1OuXzWZz+Z5fVnE4HBo0aJCeeOIJNWrUyOpyTGP5rraudvz4cdnt9t+trhoREaFdu3ZZVJV5HA6HRo0apfbt26tx48ZWl+MSn3/+uRITE7Vu3TqrSzHFvn37NG3aNI0ePVpPP/201q1bpxEjRsjPz0+DBw+2ujynGzNmjLKyslS/fn15e3vLbrfr5Zdf1sCBA60uzeXS0tIk6aKvX+dv82R5eXl66qmndO+993rMxmu/NWHCBPn4+GjEiBFWl2Iqjw8fZd3w4cO1bds2rVixwupSXCI1NVUjR47U/PnzFRAQYHU5pnA4HGrZsqVeeeUVSVLz5s21bds2vfvuux4ZPr788kvNnDlTs2bNUqNGjbRp0yaNGjVKUVFRHvl8UaywsFD9+/eXYRiaNm2a1eW4xIYNGzR58mQlJibKZrNZXY6pPP6yS6VKleTt7a309PQLjqenpysyMtKiqszx6KOP6ocfftDixYtVvXp1q8txiQ0bNigjI0M33HCDfHx85OPjo6VLl2rKlCny8fGR3W63ukSnq1q1qho2bHjBsQYNGujgwYMWVeRaTzzxhMaMGaMBAwYoPj5egwYN0t///neNHz/e6tJc7vxrVFl7/TofPFJSUjR//nyPHfVYvny5MjIyFBMTU/L6lZKSoscff1w1atSwujyX8vjw4efnpxYtWmjhwoUlxxwOhxYuXKi2bdtaWJnrGIahRx99VLNnz9aiRYsUFxdndUku06VLF23dulWbNm0q+WrZsqUGDhyoTZs2ydvb2+oSna59+/a/mzq9Z88excbGWlSRa+Xm5srL68KXKm9vbzkcDosqMk9cXJwiIyMveP3KysrSmjVrPPb163zwSEpK0oIFCxQeHm51SS4zaNAgbdmy5YLXr6ioKD3xxBP6+eefrS7PpcrEZZfRo0dr8ODBatmypVq1aqVJkyYpJydHQ4YMsbo0lxg+fLhmzZql7777TsHBwSXXhkNDQxUYGGhxdc4VHBz8u16WoKAghYeHe2yPy9///ne1a9dOr7zyivr376+1a9dq+vTpmj59utWlucTtt9+ul19+WTExMWrUqJE2btyoiRMnaujQoVaX5hRnzpxRcnJyyff79+/Xpk2bFBYWppiYGI0aNUovvfSS6tSpo7i4OI0bN05RUVHq27evdUVfhz96vlWrVtXdd9+txMRE/fDDD7Lb7SWvX2FhYfLz87Oq7Gt2ud/vb8OVr6+vIiMjVa9ePbNLNZfV023M8vbbbxsxMTGGn5+f0apVK2P16tVWl+Qyki769eGHH1pdmik8faqtYRjG999/bzRu3Njw9/c36tevb0yfPt3qklwmKyvLGDlypBETE2MEBAQYNWvWNJ555hkjPz/f6tKcYvHixRf9/zp48GDDMIqn244bN86IiIgw/P39jS5duhi7d++2tujr8EfPd//+/Zd8/Vq8eLHVpV+Ty/1+f6usTLW1GYaHLBMIAADcgsf3fAAAgNKF8AEAAExF+AAAAKYifAAAAFMRPgAAgKkIHwAAwFSEDwAAYCrCBwAAMBXhAwAAmIrwAQAATEX4AAAApiJ8AAAAU/0fbiBCr9lDU6QAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.ylim(bottom = 0, top = max(data)*1.4)\n", - "plt.plot(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "def create_dummies(data, quarters):\n", - " dummies = np.zeros((len(data), len(quarters)))\n", - " \n", - " for i in range(len(dummies)):\n", - " dummies[i][i%len(quarters)] = 1\n", - "\n", - " return dummies[:,:-1]\n", - "\n", - "dummies = create_dummies(data, Quarter)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "b_0 = m.continuous_var(name = 'Intercept', lb = -10e6)\n", - "b_1 = m.continuous_var(name = 'Trend', lb = -10e6)\n", - "b_x = m.continuous_var_list(len(Quarter)-1, lb = -10e6, name = 'Slope')\n", - "\n", - "mse = []\n", - "for i in range(len(data)):\n", - " mse.append((b_0 + b_1 * Year[i%len(Year)] + b_x @ dummies[i] - data[i])**2)\n", - "\n", - "m.minimize(sum(mse))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 7\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 5\n", - " Integer space required = 5\n", - " Total non-zeros in factor = 15\n", - " Total FP ops to factor = 55\n", - "Tried aggregator 1 time.\n", - "QP Presolve eliminated 1 rows and 1 columns.\n", - "QP Presolve added 0 rows and 5 columns.\n", - "Reduced QP has 4 rows, 9 columns, and 16 nonzeros.\n", - "Reduced QP objective Q matrix has 4 nonzeros.\n", - "Presolve time = 0.02 sec. (0.00 ticks)\n", - "Parallel mode: using up to 4 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 5\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 4\n", - " Rows in Factor = 4\n", - " Integer space required = 4\n", - " Total non-zeros in factor = 10\n", - " Total FP ops to factor = 30\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 3.2103893e+15 -3.2104192e+15 2.71e+06 0.00e+00 1.41e+09\n", - " 1 5.0913834e+13 -5.0976514e+13 3.41e+05 0.00e+00 1.77e+08\n", - " 2 1.3292638e+09 -1.8127584e+09 1.74e+03 0.00e+00 9.06e+05\n", - " 3 3.2901991e+04 -2.4504107e+06 8.72e+00 0.00e+00 4.53e+03\n", - " 4 6.4394786e+00 -1.2079451e+04 4.36e-02 0.00e+00 2.27e+01\n", - " 5 7.2940343e+00 -5.3127135e+01 2.18e-04 0.00e+00 1.13e-01\n", - " 6 7.3024547e+00 7.0003519e+00 1.13e-06 0.00e+00 5.66e-04\n", - " 7 7.3025155e+00 7.2651676e+00 1.42e-07 0.00e+00 1.05e-05\n", - " 8 7.3025017e+00 7.2911072e+00 2.01e-08 0.00e+00 1.76e-06\n", - " 9 7.3024982e+00 7.3002799e+00 8.38e-08 0.00e+00 7.64e-14\n", - " 10 7.3024980e+00 7.3020560e+00 1.40e-08 0.00e+00 1.59e-13\n", - " 11 7.3025000e+00 7.3024112e+00 3.27e-09 0.00e+00 1.70e-13\n", - " 12 7.3025000e+00 7.3024822e+00 9.59e-09 0.00e+00 1.14e-13\n", - " 13 7.3024989e+00 7.3024964e+00 1.12e-08 0.00e+00 1.59e-13\n", - "Barrier time = 0.06 sec. (0.03 ticks)\n", - "\n", - "Total time on 4 threads = 0.06 sec. (0.03 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model10\n", - "objective: 7.30251\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Intercept=-168630.578\n", - "Trend=42159.526\n", - "Slope_0=126476.777\n", - "Slope_1=84316.727\n", - "Slope_2=42159.076\n", - "\n" - ] - } - ], - "source": [ - "print(solution) # The numbers seem way to dumb. But I will trust it." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/TextbookSales_Problem.ipynb b/Time Series/TextbookSales_Problem.ipynb deleted file mode 100644 index 5e17869..0000000 --- a/Time Series/TextbookSales_Problem.ipynb +++ /dev/null @@ -1,178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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VhU2bNuHWW28FALz55puQy+WYOHEijEYj4uPj8cEHH1jvr1AosG7dOjz22GOIi4uDi4sLpk2bhhdeeMF6TGhoKNavX485c+bg7bffRmBgIJYvX474+HgbvWQiIiL7tfDHw1iVlosHYoPx9LhwaBwdxC7JLrV4HRh7xXVgiIhIakxmATEvJaOkogYA4KdxxEvj+2B0hO8N7tl+tPo6MERE7cXh83os3nAMl8urxS6FOrgj+XqUVNTARaVAFy9n6AxVePiL/Zi16gCKyoxil2dXGGCIqMN7cd1RfJRyBg99thflxtob34Golew8VQQAiOvmjY2zh+MfN3eFQi7DuswLGP1GCr47cA7tdOCkyRhgiKhDq6ox4WBeCQDg0Dk9Hlt5ANW1ZnGLog5rV32Auam7NxwdFFgwrhd++OdQ9PLXoKSiBklrDmHaZ/twrrhC5ErFxwBDRB3aobwSVNea4eaohJODAjtOXMT8bzNhNvNbLrWtqhoT9p0tBgAMDfO2Xh8ZqMVPs4ZibnxPqJRy7DhxEWPe3IEVu3I69N8pAwwRdWhpOZcBAMN7dMKyB/tDKZfh+4PnsWTjcZEro45m/9liVNea4adxRLdOLg1uc1DIkTgyDBuevAkDu3igotqE534+ir99lIpThaUiVSwuBhgi6tD21geY2FBPjOjpg6X3RAEAPt5xBv/dcUbM0qiDsfS/DA3zhkwmu+ox3Tq5YvWjcXhxfB+4qBRI/70Yt729E+9uOdnhhj4ZYIiow6oxmZH+e90p+9hQLwDAhP6BWDAuHADw8i/H8P3Bc6LVRx3LzlMXAQDDuntd9zi5XIYpg0OQnHQzbgn3QbXJjNeTT+DO93biUH0/V0fAAENEHVbWeT0qa0xwd3ZAdx9X6/WPDu+KGcNCAQBz12Yi5cRFsUqkDuJyeTWO5BsANOx/uZ4Adyd8Mm0A3p7UF54uKhzXleLuD3bh5fVHUVltas1y7QIDDBF1WJbho0FdPCGX/3HKXiaT4T+39cJdfQNQaxbw2P/SO9Q3W2p7qacvQRCAnr5u8HFzbPT9ZDIZ7urbGclzhuOuvgEwC8B/f8tB/Fs7sLt+SKq9YoAhog4r7cwlAMCgUM+/3CaXy/DaPdG4qbs3KqpNeGjFPuQUlbd1idRBXNn/0hxermq8PakfPv37APhrHZF7uQIPLE/D/G8yoa+ssWWpdoMBhog6JJNZwP6zDftf/kyllGPZgzGI7KzF5fJqTP00DYWlVW1ZJnUQlvVfbtT/ciO3hPvi1znDMWVwCABg9f483PpGCjYe1rW4RnvDAENEHdKxCwaUGmvhqlYiIuDa+624qpX47KGBCPFyRt7lSvz9030orWqf32hJHLmXKpB7uQJKuQyDrhGmm8LN0QEvju+DNf+IQ1dvFxSWGjHzf+n458r0dhXAGWCIqEOyrP8yoIsHFPKrT1m18HZV44vpg+DtqsLRCwb848t0GGvbf5MktQ3L8FH/YA+4qpU2e9xBoZ745cmbkDiyGxRyGX7J0uHWN3Zg7f68drEdAQMMEXVIe3Pq+l+uNXz0ZyFeLljx0CC4qBTYffoSktYc6tCroJLt7Gph/8v1ODooMDc+HD/NGoo+nTXQV9Zg7jeZmPrpXuRdlvZ2BAwwRNThCILwxwykqzTwXkufzlp8NGUAHBQyrM+8gBfWHW0X32RJPGazgF2nbdP/cj29A7T44Z9DsWBcONRKOX47WYQxb+7A8t/OwCTRIM4AQ0QdzsnCMhRX1MDRQY7Iztom3XdYd2+8fm9fAMCK3WfxwfbTrVAhdRRHLxhQUlEDV7USUYHurfpcSoUc/7i5GzbOHo7YUE9U1pjw0vpjmLhsN7J10tuOgAGGiDocS/9LTIgHVMqm/zd4Z3QAFt0eAQB4bVM21uzPs2l91HH8drLu7Mvgrp5wULTNR3Kotwu+emQwXrk7Em5qJTLySnD7u7/hzeQTkurtYoAhog7Huv5Ll+afsp8+LBQzb+4GAFjwXRa2HCuwSW3UsVinT7dC/8v1yOUyPBAbjOSkmzG6ly9qTALe3nISt7+zEwdyi9u0luZigCGiDuXK/pfYro3vf7ma+WN7YmL/QJjMAhJXHbDuq0TUGFU1Juw9W/e3OKx72wYYCz+tI/47NQbvPdAPXi4qnCwsw8Rlu/H8z0dQbqwVpabGYoAhog7l90sVKCw1QqWQo2+Qe4seSyaTYcnESIzs2QlVNWbM+HwfThVKr5eAxJH+ezGqa83w1ajRrZPrje/QSmQyGW6PCsDmpJsxoX9nCALw2a6ziH9rB347ab/7gDHAEFGHklY/fTo6SAtHB0WLH89BIcf7k/ujb5A7SipqMPWTvbigr2zx41L7d+X2ATLZ9dciagseLiq8cW9frHhoIDq7O+FccSWmfLIXT605hJKKarHL+wsGGCLqUCwNvI1d/6UxnFVKfPr3gejayQX5+ipM+3Qv9BVcrZeub+dJcfpfbmRETx/8Omc4/j6kC2Qy4NsD5zD6jRSsz7xgV8sGMMAQUYeSdqbp6780hqeLCl9MHwRfjRonCsrw8Bf7UFUjnRkd1LaKy6txOF8PwP4CDAC4qJV47s7e+GbmEIT5uKKorBqJqw7gH1+mo8BgH9sRMMCQKPJLKrH8tzPYdaqI/8lTmzlXXIHzJZVQyGWICfGw+eMHejjj8+mD4OaoxL6zxXjiq4OoNZlt/jwkfalnLkEQgB6+rvDROIpdzjXFhHhg/RPD8MQtYVDKZfj1aAFGv5GCr/fmin42hgGGRLHoxyN4af0xTF6ehqjnfsV9H6Xi7c0nse/sZVTX8j98ah376md89OmshYsN95y5UrifBsunDoBKKcevRwvwzI9HRP+PnuzPzlbcPsDW1EoFksb0xM+PD0N0oBalVbV4+rssPPDfNJwtKhetLgYYanNVNSbsPFXX2e7tqka1yYy0nMt4c/MJ/O3DVEQ//yumfJKGZdtPIyOvhN9gyWYsw0exNh4++rPYrl54Z1JfyGXAV3tz8dbmk636fCQ9Yq3/0hK9/DX47p9DsTChFxwd5Eg9cwm/HtWJVk/rfAUhuo49Zy6hqsYMP40jUhfcgrOXKrD7dBFST19C6ulLuFRejd9OFllXqHRTKxHb1RODu3phSDdvhPu5QX6D3YOJrsa6/ksrBxgAGNvHHy/c1QcLfziMt7ecRCc3NR4cHNLqz0v2L+9yBX6/VAGFXIbYrq23/1FrUMhlePimrrg1whef7TqL6UNDRauFAYba3PbsurMvI8M7QSaTIdTbBaHeLpgcGwJBEHCioAypp4uw+/Ql7DlzCYaqWmw+VojNxwoBAB7ODvVhxgtx3bzRrZOLXUxBJPtWaKjCmaJyyGTAgC6tH2AA4MHBISgsNeKdLSex6MfD8HZVY2wfvzZ5brJfluGjfkHucG2loczWFuLlgufu7C1qDdL8zZFkCYKArcfrgsjInj5/uV0mk6Gnnxt6+rnh70NDYTILOHbBgN31gWZfzmUUV9Rgw2EdNhyuO3XZyU2NId3qA01XbwR5OjHQ0F9YVjzt5aeB1smhzZ53zujuuFhqxFd7c/HE1wfx5fRBkvvWTbZlCTBirb7bXjDAUJvKKSpH7uUKOChkjWpeU8hl6NNZiz6dtXh0eDfUmMzIPKdH6ukipJ65hP1ni3Gx1IgfM/LxY0Y+AKCzu1P92Zm6i7/WqbVfFkmAZfjI1tOnb0Qmk+HFu3qjqMyI5KMFePiL/Vg7Mw7hfpo2rYPsg9ksYLcE+1/sEQMMtalt9cNHsaFezZoF4qCQIybEAzEhHph1S3dU1ZhwMLcEqWcuIfV0EQ7mluB8SSXWpp/D2vRzAICu3i4YXH+GZnBXL3i7qm36mkga2qqB92qUCjnevb8fpnyShn1nizHt07349rEhCPRwbvNaSFxHLxhQXFEDF5UC0S3cyqKja9IspMWLF2PgwIFwc3ODj48Pxo8fj+zs7AbHjBgxAjKZrMFl5syZDY7Jzc1FQkICnJ2d4ePjg7lz56K2tuGmUdu3b0f//v2hVqsRFhaGFStWNO8Vkl3Znl03fDSiZyebPJ6jgwJx3byQdGsPrJ05BJnPjcEX0wdh5s3dEB2ohVwGnCkqx6q0XMxadRADXtqM+Dd34LmfjmDTER1XS+0gisurkV1Qt0dRW5+BsXB0UGD51IHo4euKAoMRUz/di8vl9rc8O7Uuy+yjwV294KDgROCWaNJX4JSUFCQmJmLgwIGora3Fv//9b4wZMwZHjx6Fi4uL9bhHHnkEL7zwgvVnZ+c/vmWYTCYkJCTAz88Pu3fvxoULFzB16lQ4ODjglVdeAQDk5OQgISEBM2fOxMqVK7FlyxY8/PDD8Pf3R3x8fEtfM4mk3Fhr/RY8Mvyv/S+24KxSYniPThjeoy4gGapqsPfMZew+fQmpZy7h2AUDsgtKkV1QihW7z0ImA/oEaK3DTQO7eEq2qY6uzdL/EubjCi8Rz8BpnR3w+fRBmPjBbpy5WI7pK/Zh1SOxcFbxb66jkNL6L/ZOJrRghaWLFy/Cx8cHKSkpGD58OIC6MzB9+/bFW2+9ddX7bNiwAbfffjvy8/Ph6+sLAPjwww8xf/58XLx4ESqVCvPnz8f69etx+PBh6/0mTZqEkpISbNy4sVG1GQwGaLVa6PV6aDQca7YHyUcL8MgX+xHs6YyUuSNEabS9XF6NPWfqpmvvPl2E0xcbLsKklMsQFajFkG7eGNLNC/1DPGyy4R+J68V1R/HJzhw8EBuMV+6OFLscnCosxT0fpqKkogYje3bCx1MH8Nt4B1BVY0L087/CWGtG8pzh6O7rJnZJdqmxn98tesfo9XX7OHh6Njwlu3LlSnh7e6NPnz5YsGABKioqrLelpqYiMjLSGl4AID4+HgaDAUeOHLEeM3r06AaPGR8fj9TU1GvWYjQaYTAYGlzIvmzLtsw+6iTaLCFPFxVui/THi+P7YMtTI5D271F4e1Jf3DcgCEGeTqg1CziQW4L3tp3CA8vTMPClzUg9fUmUWsl22nL9l8YI83HDJ9MGwtFBjm3ZF/H0t1lcrbcDOPB7MYy1Zvi4qRHm4yp2OZLX7POWZrMZs2fPxtChQ9GnTx/r9Q888ABCQkIQEBCAzMxMzJ8/H9nZ2fjuu+8AADqdrkF4AWD9WafTXfcYg8GAyspKODn9dVbJ4sWL8fzzzzf35VArEwQB2+qnT49opeGj5vDVOOKuvp1xV9/OAOoWmEqtP0Oz61QRCkuNmLM6Axtn3wR3Z5XI1VJzGKpqcKR+0zxb7kDdUjEhHnj/gf549Mt0fHvgHHw0aswfGy52WdSKdl4x+4hLPbRcswNMYmIiDh8+jJ07dza4/tFHH7X+OzIyEv7+/hg1ahROnz6Nbt26Nb/SG1iwYAGSkpKsPxsMBgQFBbXa81HTZBeU4oK+CmqlHHF2vAZGkKczgjydce+AIFRU1+L2d3biTFE5nvnxCN69v5/Y5VEzpP9eDLMAhHg5w09rX5vmjerli8UTIjHvm0ws234aPm5qPCTiyqbUunax/8WmmjWENGvWLKxbtw7btm1DYGDgdY+NjY0FAJw6dQoA4Ofnh4KCggbHWH728/O77jEajeaqZ18AQK1WQ6PRNLiQ/dh2vG769JBuXpLpKXFWKfHGfX2hkMvw86F8/JhxXuySqBms67+00eq7TXXvgCDMje8JAHhh3VH8fChf5IqoNZRUVCPzfN2ZQAYY22hSgBEEAbNmzcL333+PrVu3IjT0xt8UMjIyAAD+/v4AgLi4OGRlZaGwsNB6THJyMjQaDSIiIqzHbNmypcHjJCcnIy4urinlkh2x9r/Y0fBRY/QNcsfjt4QBAJ754TDySypFroiaKu1MXQ+TWNOnG+OfI7phWlwIBAFIWpNh/aZO7Ufq6UsQBKC7j6vdnQmUqiYFmMTERPzvf//DqlWr4ObmBp1OB51Oh8rKuv/UT58+jRdffBHp6ek4e/YsfvrpJ0ydOhXDhw9HVFQUAGDMmDGIiIjAlClTcOjQIWzatAkLFy5EYmIi1Oq66Y0zZ87EmTNnMG/ePBw/fhwffPAB1qxZgzlz5tj45VNb0FfWIP33YgDAiB7SCjAAMGtkGKKD3GGoqsW/1h6C2cxmS6morDYh81zdt97Bdjx0KZPJsOiO3kiI9EeNScA/vkzH4fpv69Q+cPq07TUpwCxbtgx6vR4jRoyAv7+/9bJ69WoAgEqlwubNmzFmzBiEh4fjqaeewsSJE/Hzzz9bH0OhUGDdunVQKBSIi4vDgw8+iKlTpzZYNyY0NBTr169HcnIyoqOj8frrr2P58uVcA0aidp4sgsksoFsnFwR7SW/lUaVCjjfvjYaTgwK7T1/CZ7vPil0SNdKB3GLUmgX4ax0R6GHfW0oo5DK8cV804rp6ocxYi79/the/Xyq/8R1JEnZx+wCba1IT742m+QUFBSElJeWGjxMSEoJffvnluseMGDECBw8ebEp5ZKf+mD4tvbMvFl07ueI/Cb2w8IfDeHXjcdzU3Rs9uIaD3Uu7Yvq0FGZ9qJUKfDQ1Bvd9tAfHLhgw9dO9+GbmEHRy4/YXUpZ3uQJnL1VAIZchtqv9DmVKDVdOolZlNgvYXr//kdT6X/5scmwwRvbshOpaM2Z/nYHqWrPYJdEN7M2x9L/Y7/DRn2kcHfD5QwMR6OGE3y9V4KEVe1FmrL3xHcluWc6+9A1yh5tj2+2E3t4xwFCrOpJvQFGZES4qBQZ08RC7nBaRyWR49Z4oeDg74OgFA97cfELskug6jLV1G30C9t3AezU+Gkd8MX0QPF1UOHzegJlfpjMwS9hODh+1CgYYalWW4aOhYd5QK6Uxffp6fNwcsXhC3VL0H6acxr76PXbI/mSe08NYa4a3qwrdOrnc+A52pmsnV3z294FwVimw81QRG8glymwWsLt+Ne9h3RlgbIkBhlqVVKdPX8/YPv64JyYQggDMWZ2B0iruaG2Prpw+LYX+l6uJDnLHsgdjoJTL8NOhfLz8yzFuOSAxx3QGXC6vhotKgb5B7mKX064wwFCruVxejYy8EgDSbuC9mmfviECghxPOFVfihZ+Pil0OXcUfDbzS6X+5mpt7dMJrf6tbhuKTnTl4f9spkSuiprD0v8R29eKGnTbG3ya1mh0nLkIQgF7+mna3cJObowPeuLcvZDJgbfo5bDysE7skukKtyWxde0hq/S9Xc3e/QCxM6AUA+L9fT+CTnTkiV0SNtfNU3ZlArv9iewww1Gqu3H26PRoU6ol/DK/b3+vf32ehsLRK5IrI4nC+ARXVJmidHNCznUx3f/imrpg9ujsA4MV1R/HV3lyRK6IbqaoxWWfC3cT+F5tjgKFWYTILSDnRPqZPX8+cW7ujl78Gl8urMf+bTPYn2AnLh8bALp6Qy6XZ/3I1T47qjn8M7wqgLjT/cJD7c9mzA7nFqKoxo5ObGt19XMUup91hgKFWkZFXgpKKGmgclejXjhvX1EoF3rqvL1RKObZlX8Qqfiu2C2ln/ljArj2RyWR4elw4pgyu2zfpqbWHOHxpx65cfVeqjeT2jAGGWsX2+uGj4T06QdnOG9d6+rlhXv1uwi+tO4acIi7/LiaTWcDe+unt7XHVU5lMhufv7I17YgJhMgt4/KsD1uFasi/sf2ld7fuThUSz9bj0tw9oiulDQzGkmxcqa0yYszoDtSYuOiaWbF0pSqtq4aJSIMJfI3Y5rUIul+HViVFIiKrb/HHml+lIrV9rhOyDvqIGWedKAHABu9bCAEM2V2CowpF8AwDg5nbawPtncrkM//e3aLg5KpGRV4L3t50Wu6QOK62+/yWmi2e7PvunkMvw5r19MSrcB8ZaM2Z8vg8HcovFLovqpZ4pglkAwnxc290sTHvRft/dJJqU+r2PogO18HbtOJvQBbg74aXxfQAA72w9iUP1a+BQ29qb0z77X65GpZTj/cn9MSzMGxXVJkz7dC8On9eLXRaB2we0BQYYsjnLePyIDjJ8dKU7owNwe5Q/TGYBc1ZnoLLaJHZJHYogCB0qwACAo4MCH0+NwYAQD5RW1WLqp3txsqBU7LI6vF3sf2l1DDBkUzUmM347WffNoz1Pn74WmUyGl8b3gZ/GEWeKyrF4wzGxS+pQTl8sw6XyaqiVckQFuotdTptxVinx6UMDERWoxeXyakxenoazbCYXzbniCuQUlUMhl7XLRnJ7wQBDNrX/bDHKjLXwclEhqrNW7HJE4e6ssi79/kXq79YZWdT6LNsH9A/2gErZsf570zg64POHBqGnrxsKS42YvDwN50sqxS6rQ9pdf/YlOlALjaODyNW0Xx3rHU6tzvJhfXOPTu1qAbGmuql7J/x9SBcAwNxvMlFcXi1uQR2EZf2X9rB9QHN4uKjwv4dj0dXbBedLKjH5v3tQaOAK0W3tN/a/tAkGGLIpa/9LBxw++rOnx4WjWycXXCw14t/fZ3GV3lbWoP+lA5+27+SmxspHYhHo4YSzlyrw4CdpuMwA3WbMZgG7LQGme8eYhSkWBhiymXPFFThRUAa5DBjOfT/g6KDA25P6QSmXYcNhHb47wGXfW1Pu5QroDFVwUMjQL8hD7HJE5a91wqqHB8NXo8aJgjJM/TQN+soascvqEI7rSnGpvBrOKgX6tuNVyO0BAwzZzPb66dP9gz3g7qwSuRr70KezFnNu7QEAePanI8i7XCFyRe2Xpf8lOtAdTiqFyNWIL9jLGSsfHgwvFxUOnzdg+op9KDfWil1Wu2fZPiA21LPD9WG1Nf52yWYs/S8dcfbR9fxjeFfEhHigzFiLp9YegsnMoaTWYBk+6qj9L1cT5uOKL2fEQuOoRPrvxXjki/2oquHU/tZkWf+F06dbHwMM2URVjcm67sGIDrL6bmMpFXK8cW80XFQK7M25jE92nhG7pHbJsgIvA0xDEQEafD59EFxUCuw+fQmP/S8d1bXc6qI1GGtN1r/DYRxGb3UMMGQTe3Muo7LGBF+Nut3uP9MSIV4uWHRHBADg/zadwLELBpEral/ySyqRd7kSchkwoAsDzJ/1C/bAp38fCEeHul3TZ68+yP26WsGB30tQVWOGt6saPX3dxC6n3WOAIZuwzD4a2dOH28Zfw70DgjC6ly+qTWbMWZ3BU/k2tK9+9+k+nbVwVStFrsY+xXb1wkdTBkClkOOXLB3mfZMJM4czbWqXdfq0F/8fbAMMMGQTlgbejrh9QGPJZDIsmRgJLxcVjutK8UbyCbFLajf2WNZ/4dmX67q5Rye890A/KOQyfHfwPJ758TCn99sQ+1/aFgMMtVhOUTlyisrhoJBhaJiX2OXYNW9XNV6dWLdK739/O4PU05dErqh92FvfdxDblX9/NzKmtx/euDcaMhmwMi0XL68/xhBjA/rKGmSeKwHAANNWGGCoxSyzjwZ28YQbl82+odERvrh/UBAEAXhqTQYMVVyfoyUulhpx+mI5ZDJgYJeOvf5LY93VtzNenVAXpJfvzMGbm0+KXJH0pZ6+BLMAdO3kggB3J7HL6RAYYKjFth7/o/+FGmdhQgSCPZ2Rr6/Ccz8eEbscSbP0v/T0deP6Q01w78AgPFffWP7OlpP4MOW0yBVJm6X/5SaefWkzDDDUIhXVtdb9Z0aGc/p0Y7molXjzvmjIZcB3B89jfeYFsUuSLOv2AZw+3WR/HxqKeWN7AgCWbDiOL1LPiluQhO1i/0ubY4ChFtl96hKqTWYEejihWydXscuRlJgQT/xzRBgA4D8/ZKGAm+41y54zlvVf2P/SHP8cEYbHb6n7O1z04xGs2Z8nckXSc76kEmeKyiGXAYO78e+wrTDAUItw+nTLPDm6OyI7a1FSUYN/rT3EZsomKqmoRnZBKQAuYNcSSbf2wPShoQCAp7/NxM+H8kWuSFosZ1+ig9yhYR9gm2lSgFm8eDEGDhwINzc3+Pj4YPz48cjOzm5wTFVVFRITE+Hl5QVXV1dMnDgRBQUFDY7Jzc1FQkICnJ2d4ePjg7lz56K2tuEeHdu3b0f//v2hVqsRFhaGFStWNO8VUqsRBME6fZrDR83joJDjzfuioVbK8dvJIny553exS5KUfWeLIdQ3TnZyU4tdjmTJZDI8c3sv3D8oGGYBmLM6A8lHC258RwJw5fovHD5qS00KMCkpKUhMTMSePXuQnJyMmpoajBkzBuXl5dZj5syZg59//hlr165FSkoK8vPzMWHCBOvtJpMJCQkJqK6uxu7du/H5559jxYoVWLRokfWYnJwcJCQkYOTIkcjIyMDs2bPx8MMPY9OmTTZ4yWQrJwvLcL6kEiqlHHFd+cZtrjAfNywYFw4AeOWXYzhVWCZyRdJhnT7N4aMWk8lkeHl8H9zdrzNqzQISVx7Abycvil2W3TObBQYYsQgtUFhYKAAQUlJSBEEQhJKSEsHBwUFYu3at9Zhjx44JAITU1FRBEAThl19+EeRyuaDT6azHLFu2TNBoNILRaBQEQRDmzZsn9O7du8Fz3XfffUJ8fHyja9Pr9QIAQa/XN/v10fV9uP2UEDJ/nTD1kzSxS5E8k8ksPLh8jxAyf51wx7u/CdW1JrFLkoQ73/1NCJm/Tvj+wDmxS2k3ampNwj++2C+EzF8n9Fz4i5B25pLYJdm1o/l6IWT+OiF84QbBWMP3rS009vO7RT0wer0eAODpWTf2nJ6ejpqaGowePdp6THh4OIKDg5GamgoASE1NRWRkJHx9fa3HxMfHw2Aw4MiRI9ZjrnwMyzGWx7gao9EIg8HQ4EKt64/+Fw4ftZRcLsNr90RD6+SAzHN6vLuF63LcSJmxFofz697n7H+xHaVCjnfu74cRPTuhqsaM6Sv24VBeidhl2S3L2ZfYrp5QKdlW2paa/ds2m82YPXs2hg4dij59+gAAdDodVCoV3N3dGxzr6+sLnU5nPebK8GK53XLb9Y4xGAyorKy8aj2LFy+GVqu1XoKCgpr70qgRDFU12H+2GAC3D7AVP60jXr677r303rZTOJBbLHJF9i3992KYzAKCPJ24cJiNqZRyfPhgDAZ39USZsRZTP93LDUivYSeHj0TT7ACTmJiIw4cP4+uvv7ZlPc22YMEC6PV66yUvj1MBW9Ouk0WoNQvo6u2CLt4uYpfTbtweFYDxfQOsjZTlxtob36mDSrNMn+7C/pfW4OigwPJpA9Ev2B36yhpM+SSN/Vl/Ul1rtq6DxfVf2l6zAsysWbOwbt06bNu2DYGBgdbr/fz8UF1djZKSkgbHFxQUwM/Pz3rMn2clWX6+0TEajQZOTlf/pqVWq6HRaBpcqPVYho949sX2nr+rDwK0jvj9UgVeWn9M7HLslnUBu64cPmotrmolVjw0CL0DNCgqq8aDy9OQd7lC7LLsxoHcYlTWmODtqkK4n5vY5XQ4TQowgiBg1qxZ+P7777F161aEhoY2uD0mJgYODg7YsmWL9brs7Gzk5uYiLi4OABAXF4esrCwUFhZaj0lOToZGo0FERIT1mCsfw3KM5TFIXIIgYBunT7carZMD/u/eaADAV3tzseUYp7P+WVWNCYfqN87jCrytS+vkgC9nxKK7jyt0hio8sHwPLuivPpTf0Vy5+i7XwWp7TQowiYmJ+N///odVq1bBzc0NOp0OOp3O2pei1WoxY8YMJCUlYdu2bUhPT8dDDz2EuLg4DB48GAAwZswYREREYMqUKTh06BA2bdqEhQsXIjExEWp13ToOM2fOxJkzZzBv3jwcP34cH3zwAdasWYM5c+bY+OVTcxzJN+BiqRHOKgWbJ1vJkG7eeHhY3ReE+d9m4lKZUeSK7MuB3GLUmAT4atQI9nQWu5x2z9NFhZUPxyLEyxl5lysxeXkaLpbyb3Intw8QVZMCzLJly6DX6zFixAj4+/tbL6tXr7Ye8+abb+L222/HxIkTMXz4cPj5+eG7776z3q5QKLBu3TooFArExcXhwQcfxNSpU/HCCy9YjwkNDcX69euRnJyM6OhovP7661i+fDni4+Nt8JKppSy7Tw8N84ZaqRC5mvbrX/E90dPXDUVl1Xj6uyyu0nuFP/Y/8uI33zbio3HEyodjEaB1xJmL5ZjySRpKKqrFLks0hqoa6+wsBhhxyIR2+r+iwWCAVquFXq9nP4yNTVy2G+m/F+OVuyPxQGyw2OW0a0fzDbjr/Z2oMQlYOjEK9w7k7DoAuP/jPUg9cwkvje+DBweHiF1Oh5JTVI57P0rFxVIjogO1+N/DsXDrgMvn/3pEh0e/TEdXbxds/dcIsctpVxr7+c1J69QkxeXVOJhrmT7N/pfWFhGgwVNj6nYLfv7nI8i9xAbK6lqzdYr5YDbwtrlQbxesfDgWHs4OOHROjxkr9qOiuuPNlrNOn+7Osy9iYYChJtlx8iLMAhDu58a1N9rIIzd1xaAuniivNiFpTQZM5nZ50rTRss6XwFhrhqeLijugi6SHrxu+nBELN0cl9p69jH98mQ5jrUnsstoU+1/ExwBDTbLtOKdPtzWFXIbX742Gq1qJ/b8X48OU02KXJKo99etuDOriyf4XEfXprMWKhwbCWaXAbyeLkLjyIGpMZrHLahP5JZU4c7EcchkwuCvXIRILAww1msksIOVE/fRpDh+1qSBPZzx3Z28AwJvJJ3D4vF7kisTD9V/sR0yIJ5ZPHQCVUo7NxwowZ3XHOENomT4dFegOrVPH6/+xFwww1GiHzpWguKIGbo5K9A/xELucDmdi/84Y29sPtWYBc1ZnoKqmY52yB4Bakxnpv9f1v3AKv30YEuaNjx6MgYNChnWZF/D0t5kwt/MQw92n7QMDDDXa9vrho+HdO8FBwT+dtiaTyfDKhEh0clPjZGEZXt14XOyS2tzRCwaUGWuhcVQi3I+zC+3FyHAfvDOpH+QyYG36OXy2+6zYJbUaQRCw81TdNhbsfxEXP4Wo0Syr73L2kXg8XVRYek8UAOCzXWex82SRyBW1Lcvw0cAunlDI2f9iT8ZF+luHOf9vU3a7nTGXXVCKojIjnBwU6B/iLnY5HRoDDDVKYWkVsur7Lm5mgBHVyJ4+eHBw3fo7/1p7CPqKGpErajvWBl4OH9mlB2NDMLirJyprTFjwfWa7XHzR8qVhUKgnF/IUGQMMNUpK/dmXyM5a+Lg5ilwN/fu2Xgj1doHOUIVnfjwsdjltwmwWsO+spYGXMz/skVwuw5IJUXB0kGPXqUtYsz9P7JJsjv0v9oMBhhplezZnH9kTZ5USb97XFwq5DD8dysfGwzqxS2p1JwpLoa+sgbNKgd4B7H+xV128XfDUrXWLL760/hgKDFUiV2Q71bVmpNUPY7L/RXwMMHRDNSYzdpys738J5/ov9qJvkDtm3twVAPDqxuPtfg2OtPrho5gQDzaR27npw0IRHeSO0qpaLPzhcLsZSsrIK0FFtQleLiqE+7mJXU6Hx/8F6IYO/F6M0qpaeDg7IDrQXexy6AqPjQiDl4sKOUXl+Hpf+ztdf6U/NnBk/4u9U8hlWDoxCg4KGZKPFmB91gWxS7KJnfVf5IaGeUPOJnLRMcDQDVlmH93coxNnftgZV7UST4zqDgB4e/NJlBvb5540giAgLadu6uqgUPa/SEFPPzf8c0QYAODZH4+guFz6O1fvZP+LXWGAoRvanl23/stIDh/ZpfsHBSPEyxlFZUYs/y1H7HJaxZmichSVVUOllCM6SCt2OdRIiSPD0NPXDZfKq/HCuqNil9MihqoaHDpXNxNzKDdwtAsMMHRd+SWVOK4rhVxWt4Ad2R+VUo5/1e9Y/fGO0ygqM4pcke1Zho/6Bblz6qqEqJRyvHpPFOQy4PuD5617qUlR2pnLMJkFhHq7oDM3srULDDB0XZbZR/2CPeDhohK5GrqWhEh/RAVqUV5twrtbTopdjs2lnakbPmL/i/T0DXLH9KGhAIB/f5+F0ipprlu0y7r7NIcw7QUDDF3XNsvwEadP2zW5XIanx4UDAFam5eJsUbnIFdlOXf8L13+RsqfG9ESwpzMu6KskuwXGb/UNvMPC+H+hvWCAaYaLpe3vFP3VGGtN1m8dI3qy/8XeDenmjZt7dEKtWcD//Zotdjk2c664Ehf0VVDKZegX7C52OdQMTioFlkyMBAD8b0+u9YyaVFzQV+L0xXLIZUAcQ7TdYIBpAkEQ8Mav2Rj66lYcyC0Wu5xWty+nGBXVJvi4qblwmETMHxsOmQxYl3kBh/JKxC7HJixnX6ICtXBWKUWuhpprSDdv3D+obguMp7/LktRu6rvqN2+MDHSH1tlB5GrIggGmCWQyGc6VVKK61ox532TCWCudN2BzbK1vuBvRsxNkMk6floKIAA3u7tsZALBkw/F2sYDYXk6fbjcW3BYOX40aOUXleHPzCbHLabQ/tg/g36A9YYBpokW3R8DbVYVThWV4f+spsctpVdbp0xw+kpSkMT2gUsiReuYSUk5cFLucFkvjAnbthsbRAS+PrxtK+u+OM8g8VyJuQY0gCIJ1/RduH2BfGGCayN1ZhRfu6gMA+GD7aRy7YBC5otZxtqgcZ4rKoZTLuOaBxAR6OGPakBAAdWdhTGbpnoXR6avw+6UKyGVATBcPscshGxgd4Ys7owNgFoB532Siuta+t8A4UVCGi6VGODrIERPCv0F7wgDTDOP6+CG+ty9qzQLmfZOJ2na4B43l7MuALh7QOHLMV2r+OSIMbo5KHNeV4oeD58Uup9ksq+9GBGj4d9iOPHtHBDycHXBcV4qPUk6LXc51Wc6+DAr14hpEdoYBphlkMhlevKsPNI5KZJ3X45Od7W/1023W3ac5fCRFHi4q6zLubySfkFTD5JX+2P+IvQftiZerGs/d2RsA8O7WUzhZUCpyRdfG/hf7xQDTTD4aRyy8PQJA3QfEmYtlIldkO5XVJqTWT3Pk9gHS9dDQLvDTOOJ8SSW+TP1d7HKaxRJgBrH/pd25MzoAo8J9UG0yY963mXY51FljMmNP/f+F7H+xPwwwLfC3mEDc1N0bxloznv4uC2Y7fAM2R+qZIlTXmtHZ3QndfVzFLoeaydFBgaRbewAA3tt2CvoKaa2AeqnMiJOFdV8MBnZhgGlvZDIZXrq7D9zUShzMLcHnu8+KXdJfZOSVoKLaBC8XFXr5cSkJe8MA0wIymQyv3B0JZ5UCe3MuY9XeXLFLsoltx+uGjzh9WvomxgSih68r9JU1WGbnvQZ/tu9s3dmXnr5u8OQ2Fu2Sv9YJC27rBQB4bVM2ci9ViFxRQ7+drBs+GhLmDbmc/xfaGwaYFgrydMbc+LqN9JZsOI78kkqRK2oZQRCu2D6Aw0dSp5DLMH9s3RYDn+3KkdTfZxqHjzqESQODMLirJyprTFjwfaZdrV3E/hf7xgBjA1PjuiAmxANlxlr8+/ssu3oDNtXpi2U4V1wJlUKOIXzTtgu3hPtgUBdPGGvNeDNZOouHpZ2x7H/EANOeyeUyLJkQBUcHOXaduoS1+8+JXRIAoLSqBhn1q1mz/8U+McDYgEIuw6sTI6FSyLE9+yJ+zMgXu6RmswwfxXb15LLt7YRMJsPTt9Wdhfn2wDlk6+x3xoeFvrIGx3R1aywNYv9Lu9fF2wVP3Vp3JvvF9UdRYKgSuaK6AG0yC+ji5YxAD2exy6GrYICxkTAfNzwxqm7a6vM/H0FRmTQ3fOTwUfvUP9gD4/r4wSwASyWwG/D+s5chCECotwt8NI5il0Nt4KGhXRAdqEVpVS0W/nBY9DPZXH3X/jU5wOzYsQN33HEHAgICIJPJ8MMPPzS4/e9//ztkMlmDy9ixYxscc/nyZUyePBkajQbu7u6YMWMGysoaTkPOzMzETTfdBEdHRwQFBWHp0qVNf3Vt7B83d0Mvfw2KK2rw3E9HxC6nyUqraqyNk5w+3f78K74nFHIZthwvtPvdgPdy+4AOR6mQY+k90XBQyJB8tAC/ZOlEreeP/hcGGHvV5ABTXl6O6OhovP/++9c8ZuzYsbhw4YL18tVXXzW4ffLkyThy5AiSk5Oxbt067NixA48++qj1doPBgDFjxiAkJATp6el47bXX8Nxzz+Hjjz9uarltykEhx2v3REEhl2Fd5gX8ekTcN2BT7Tp1CTWmulOmod4uYpdDNtatkysmDQwCACy2840e2cDbMfX0c7MuwPjsT4dRXF4tSh06fRVOFpZBJqvbRZvsU5MDzLhx4/DSSy/h7rvvvuYxarUafn5+1ouHxx/7Rxw7dgwbN27E8uXLERsbi2HDhuHdd9/F119/jfz8ut6RlStXorq6Gp9++il69+6NSZMm4YknnsAbb7zRjJfYtvp01uKRm7oCABb+cBj6SumsvWHdvJFnX9qtJ0d1h5ODAhl5Jdh42D4DdrmxFlnn9QCA2K5sJO9oEkeGoYevK4rKqvHiuqOi1GA5+xLVWQutM7ewsFet0gOzfft2+Pj4oGfPnnjsscdw6dIfp6tTU1Ph7u6OAQMGWK8bPXo05HI50tLSrMcMHz4cKtUfaz/Ex8cjOzsbxcXFV31Oo9EIg8HQ4CKW2aO7o6u3CwpLjVj8yzHR6mgKTp/uGHw0jnjkplAAdetu1NjhPl4HcothMgvo7O6Ezu5OYpdDbUylrBtKksuA7w6ex7bjhW1ewy72v0iCzQPM2LFj8cUXX2DLli149dVXkZKSgnHjxsFkqtuLRafTwcen4QekUqmEp6cndDqd9RhfX98Gx1h+thzzZ4sXL4ZWq7VegoKCbP3SGs3RQYElE6MAAF/vy7O+GezZsQulKDAY4eSg4Gn7du6R4V3h5aLCmaJyrN6XJ3Y5f2GdPs2/ww6rb5A7pg+tC9r/+T4LpVVtdyZbEARrAy/7X+ybzQPMpEmTcOeddyIyMhLjx4/HunXrsG/fPmzfvt3WT9XAggULoNfrrZe8PHH/Yx4U6ompcSEAgKe/y0RFda2o9dyI5ezL0DAvODpwx9X2zM3RAY/fUtdn8Nbmkyg32tffprWBl+u/dGhPjemJYE9n5OursHRjdps976nCMhSWGqFWytE/xOPGdyDRtPo06q5du8Lb2xunTp0CAPj5+aGwsOEpwdraWly+fBl+fn7WYwoKChocY/nZcsyfqdVqaDSaBhexzRsbjgCtI/IuV+L1X+17ATHLadoRHD7qEB6IDUGIlzOKyox2tZt6VY3JunjYIO5A3aE5qRRYMjESAPDlnt/bbOacZfuAQaGe/DJn51o9wJw7dw6XLl2Cv78/ACAuLg4lJSVIT0+3HrN161aYzWbExsZaj9mxYwdqav44bZicnIyePXs2aAi2d65qJV6ZUPcG/HRXDg7kXr1/R2wlFdXW2kb07CRyNdQWVEo5/jWmbuGwj1JO2826RRl5Jag2meHjpkYXLy4e1tEN6eaN+wfVtQM8/V0WqmpMrf6cnD4tHU0OMGVlZcjIyEBGRgYAICcnBxkZGcjNzUVZWRnmzp2LPXv24OzZs9iyZQvuuusuhIWFIT4+HgDQq1cvjB07Fo888gj27t2LXbt2YdasWZg0aRICAgIAAA888ABUKhVmzJiBI0eOYPXq1Xj77beRlJRku1feRkb09MGE/p0hCMD8bzJhrG39N2BT7ThZBLMA9PB15YqTHUhCpD8iO2tRXm3Cu1tOil0OgD+GjwaFenIjUQIALLitF3w1auQUleOtza37d1pjMmNP/ZkeNvDavyYHmP3796Nfv37o168fACApKQn9+vXDokWLoFAokJmZiTvvvBM9evTAjBkzEBMTg99++w1qtdr6GCtXrkR4eDhGjRqF2267DcOGDWuwxotWq8Wvv/6KnJwcxMTE4KmnnsKiRYsarBUjJc8kRMDbVYWThWV4f+spscv5i+3HOfuoI5LLZVgwrm6LgZVpufj9UrnIFQFpOXUfHmzgJQuNowNeHl93Jvu/v51B1jl9qz3XobwSlFeb4OHsgAh/8dsQ6PqavNnNiBEjrrsA1qZNm274GJ6enli1atV1j4mKisJvv/3W1PLskoeLCs/f2QeJqw7gg+2nMS7SH73s5M1hNgvYfqJu/yP2v3Q8Q8K8cXOPTkg5cRGvbcrGew/0F62W6loz0n+vG8rk+i90pdERvrgjOgA/H8rH3G8O4efHh8FBYfsOCMvsoyFh3pDLeQbQ3nEvpDZyW6Qf4nv7otYsYP63mai1k/U3Ms/rcbm8Gq5qJQZ0kU5/EdnO/LHhkMmAdZkXcKi+gVYMh/P1qKoxw8PZAWGdXEWrg+zTc3dEwMPZAcd1pfhw++lWeY6d9Q28N3H4SBIYYNqITCbDi3f1gcZRicxzeruZ+WGZfXRTd+9W+UZD9i8iQIO7+3YGACwRcYsBy/ovg0I9+e2X/sLLVY3n7uwNAHh36ymcLLDtruqlVTU4WB/g2f8iDfzEakM+GkcsvD0CAPBG8gnkFInfc7Cdq+8SgKQxPaBSyJF65hJS6ocU29re+v4XTp+ma7kzOgC3hPug2mTG/G8zYTLbLmzvzbkMk1lAiJczgjw5mUEKGGDa2N9iAjEszBvGWjOe/jYTZhu+AZvqYqkRh+ob4m7m9OkOLdDD2brw4pINx236wdAYJrOA/Wfr+1/YwEvXIJPJ8PLdfeCqVuJAbgk+333WZo+9k9sHSA4DTBuTyWRYPCESTg4KpOVcxqq9uaLVsqP+m3bvAA18NY6i1UH2IXFkGNwclTiuK8WPGefb9LmPXTCg1FgLN7XSbhrcyT75a52w4La62XOvbcpG3uUKmzwu13+RHgYYEQR5OmPe2LpFxJZsOI78kkpR6uDmjXQlDxcVHhvRDQDw+q8n2mTRMIu0+vVfBnTxgIL9L3QD9w8MxuCunqisMWHBd1kt7tsqMFThREEZZDJgSDcOYUoFA4xIpsZ1Qf9gd5QZa/Gf71v+BmyqWpPZegZmZDiHj6jO9KGh8NM44nxJJb5M/b3NnteyTDynT1NjyOUyLJkQBbVSjp2nirB2/7kWPZ7l7EtkZy3cnVW2KJHaAAOMSBRyGZbeEwWVQo5t2RfxY0Z+mz7/wbwSGKpq4e7sgL5BnD5NdRwdFEi6tQcA4L1tp6CvbP1dgM1mAfvO/jEDiagxuni74KkxdX+rL64/igJDVbMfi/0v0sQAI6IwHzc8MapuV+Dnfz7SpvvRWKZP39yjE0/ZUwMT+ndGdx9X6CtrsKyV1tu40snCMhRX1MDJQYHIztpWfz5qP6YPDUV0oBalVbV45ofDzTqTLQgC+18kigFGZP+4uRt6+WtQXFGD53460mbPuy27fviI/S/0J0qFHPPH1jVJfrYrBxf0rdujZZk+HRPiwbWIqEmUCjlevScKSrkMvx4twC9ZuiY/xumLZSgwGKFWyhETwrPRUsL/LUTmoJDjtXuioJDLsC7zAn490vQ3YFPp9FU4dsEAmQwY3oP9L/RXo3r5YFAXTxhrzXgz+USrPldaDoePqPnC/TT458i6M9nP/nQYxeXVTbr/b/Wr7w4K9YSjg8Lm9VHrYYCxA306a/HITV0BAM/8eLjV+w4si9f1DXKHpwsb1uivZDIZ5tdv9PhN+jmcsPGqpxaCIDDAUIsljuyGHr6uKCqrxovrjjbpvrvY/yJZDDB2Yvbo7gj1dkGBwYjFvxxr1efayt2nqRFiQjwwtrcfzAKwdOPxVnmOs5cqcLHUCJVCjr5B7q3yHNT+qZUKvDoxCnIZ8N3B89YlIm6kxmTGnvotLNj/Ij0MMHbC0aHuDQgAX+/Ls34rsDVjrcn62AwwdCNzx/aEQi7D5mOF2Ft/psSWLP0vfYPcefqeWqRfsAemDw0FAPznuyyUGWtveJ/McyUoM9bNxozgAoqSwwBjRwaFemLK4Lrl3J/+LhMV1Td+AzbV/rPFKK82wdtVjd4BfMPS9XXr5Ir7BgYBABZvOGbz9YosGzjGduXwEbVc0pgeCPZ0Rr6+Cq9uuPFZw50n6wL00G7e3EBUghhg7Mz8ceEI0Doi73IlXv/V9s2TlunTI3p24huWGmX2qO5wclDgYG4JNtm4yZz9L2RLziollkyIBAB8uef3G541tE6f7s7hIyligLEzrmolXq5/A366KwcHcott+vjcPoCaykfjiEduqjs1v3RjNmpMZps87rniCpwvqYRCLkP/YE5fJdsYEuaN+wfVnTWc/23mNbfEKDPWWv9/Zf+LNDHA2KGRPX0woV9nCAIw/5tMGGttsydN7qUKnL5YDoVcxm8c1CSPDO8KTxcVzhSVY/W+PJs8puXbcWRnLVzUSps8JhEALLitF3w1auQUleOtzSeveszenEuoNQsI9nRGkKdzG1dItsAAY6eeuT0C3q4qnCwsw/vbbLMa6vYTdWdfYkI8oHVysMljUsfg5uiAJ26pW2vjrc0nUd6IBskbsQSYWA4fkY1pHB3w8vi6M9n//e0Mss7p/3KMtf+FZ18kiwHGTnm4qPD8nX0AAB9sO4VjFwwtfsxtnD5NLfBAbAiCPZ1RVGbEJztzWvx4lv4XNvBSaxgd4Ys7ogNgMguY923mX4Y+uX2A9DHA2LHbIv0wJsIXtWYB87/NRG0Leg+qakzYfbruGwd3n6bmUCnl+Fd8TwDARymnW7R3V6GhCjlF5ZDJgJgQBhhqHc/dEQEPZwccu2DARyl/nMkuLK1CdkEpZDJgSDfugC5VDDB2TCaT4aXxfeDmqETmOT0+3dX8b72pZy7BWGuGv9YRPX3dbFgldSS3R/ojsrMW5dUmvLf1VLMfx3L2pZefhsOZ1Gq8XNV47s7eAIB3tpzCqcK6FaUtZ1/6BGjhwdXIJYsBxs75aBzxTEIEAOD1X0/gbFF5sx5nu3X6tA9kMk6fpuaRy2V4un6LgZVpv+P3S837e9zL4SNqI3dGB+CWcB9Um8yY900mTGaB/S/tBAOMBPxtQCCGhXnDWGvG/G8zYTY3bTExQRCu2H2aw0fUMkPDvDG8RyfUmAT8XzPXKmIDL7UVy5lsV7USB3JL8Pnus+x/aScYYCRAJpNh8YRIODkokJZzGV/ty23S/c8UlSP3cgUcFDJ+4yCbeHpsOGQy4OdD+cg8V9Kk+14ur0Z2/eaQA7swwFDrC3B3woLb6s4cLt5wDDpDFVRKOQZ04fpDUsYAIxFBns6YW99AufiX48gvqWz0fS2zj2JDvbjeBtlERIAG4/t2BgAs2XC8SVsM7Dtbd/alu48rvFzVrVIf0Z/dPzAYsaGeqDHV/a0O7OLB/bckjgFGQqYN6YL+we4oM9Zi4Q+HG/2hsd0yfBTO6dNkO0m39oBKIcfu05ew42TjNx+17H/E7QOoLcnlMrw6MQpqZd3H3rAwDqdLHQOMhCjq34AqhRxbjxfip0P5N7xPubEWafU7/rL/hWwpyNMZU+PqNh9dsuF4o3uz9p6t+3uM7crpq9S2uni74PV7ozGyZyf8bUCg2OVQCzHASEx3Xzc8Xr8i6nM/HbnhWhy7ThWhxiQgxMsZod4ubVEidSCJI8Pg5qjEsQsG/JBx/obHG6pqcDS/blFGNvCSGG6PCsBnDw2CN4cvJY8BRoJmjuiGcD83FFfU4Pmfj1732Cs3b+T0abI1DxcVHhvRDUDdNP9rbZxnkX62GGYB6OLlDF+NY1uUSETtFAOMBDko5Hjtnmgo5DL8fCgfyUcLrnqcIAjYdryu/2UEh4+olUwfGgo/jSPOl1Tif3t+v+6xlgXs2P9CRC3V5ACzY8cO3HHHHQgICIBMJsMPP/zQ4HZBELBo0SL4+/vDyckJo0ePxsmTDXcDvXz5MiZPngyNRgN3d3fMmDEDZWVlDY7JzMzETTfdBEdHRwQFBWHp0qVNf3XtWGSgFo/c1BUAsPCHLOgra/5yzHFdKXSGKjg6yDGY/QbUShwdFJhza3cAwHvbTl31b9HC0o81KJR/j0TUMk0OMOXl5YiOjsb7779/1duXLl2Kd955Bx9++CHS0tLg4uKC+Ph4VFVVWY+ZPHkyjhw5guTkZKxbtw47duzAo48+ar3dYDBgzJgxCAkJQXp6Ol577TU899xz+Pjjj5vxEtuv2aO7I9TbBQUGI5ZsOPaX2y3DR0O6eXO6ILWqif0D0d3HFSUVNfgw5eq7p1dU11p3BWb/CxG1VJMDzLhx4/DSSy/h7rvv/sttgiDgrbfewsKFC3HXXXchKioKX3zxBfLz861nao4dO4aNGzdi+fLliI2NxbBhw/Duu+/i66+/Rn5+3ayalStXorq6Gp9++il69+6NSZMm4YknnsAbb7zRslfbzjg6KLBkQt2W8V/tzcPuUw2nsm4/ztV3qW0oFXLMH1u3UNinO3NwQf/XdYoO5pag1iwgQOuIQA+nti6RiNoZm/bA5OTkQKfTYfTo0dbrtFotYmNjkZqaCgBITU2Fu7s7BgwYYD1m9OjRkMvlSEtLsx4zfPhwqFR/bLIVHx+P7OxsFBcXX/W5jUYjDAZDg0tHENvVC1MG101lffq7LFRU1wIA9BU1SM+t+12N6Mn1X6j1jerlg4FdPGCsNePN5L9uMZB25o/p02woJ6KWsmmA0el0AABfX98G1/v6+lpv0+l08PFp+IGqVCrh6enZ4JirPcaVz/FnixcvhlartV6CgoJa/oIkYt7YngjQOiL3cgXeqN+b5rdTF2EyCwjzcUWQp7PIFVJHIJPJ8PS4XgCAb9LP4UT9dgEWbOAlIltqN7OQFixYAL1eb73k5eWJXVKbcXN0wMv1Q0mf7srBwdxi6+wjDh9RW4oJ8UB8b1+YBWDpxuPW6421JhzMKwHAAENEtmHTAOPn5wcAKChoOK23oKDAepufnx8KCwsb3F5bW4vLly83OOZqj3Hlc/yZWq2GRqNpcOlIRvb0wYR+nWEWgHnfZCLlxB/rvxC1pXljw6GQy7D5WKF11+lDeXpU15rh7apGVy6oSEQ2YNMAExoaCj8/P2zZssV6ncFgQFpaGuLi4gAAcXFxKCkpQXp6uvWYrVu3wmw2IzY21nrMjh07UFPzx3TM5ORk9OzZEx4e3D30Wp65PQLeriqcLCxDUVk1XFQKDOBuv9TGunVyxX0D64ZwF284BkEQsLd++nRsqCf7X4jIJpocYMrKypCRkYGMjAwAdY27GRkZyM3NhUwmw+zZs/HSSy/hp59+QlZWFqZOnYqAgACMHz8eANCrVy+MHTsWjzzyCPbu3Ytdu3Zh1qxZmDRpEgICAgAADzzwAFQqFWbMmIEjR45g9erVePvtt5GUlGSzF94eebio8Pydfaw/D+vuDZWy3YwSkoTMHtUdTg4KHMwtwaYjOmv/S2xXBmoiso0mf7rt378f/fr1Q79+/QAASUlJ6NevHxYtWgQAmDdvHh5//HE8+uijGDhwIMrKyrBx40Y4Ov6xbPjKlSsRHh6OUaNG4bbbbsOwYcMarPGi1Wrx66+/IicnBzExMXjqqaewaNGiBmvF0NXdFumH2yLrhtnuiA4QuRrqqHw0jnj4plAAwNKN2Uj/vW5GHPtfiMhWZIIgNG4LWYkxGAzQarXQ6/Udrh+m1mTGycIyhPu58XQ9iaa0qgY3v7Ydl8urAQDuzg44sPBWyOX8mySia2vs5zfHF9ohpUKOXv4ahhcSlZujg3XndAAY2MWT4YWIbIYBhohazeTYEATXr0PE7QOIyJaUYhdARO2XSinHR1Ni8GNGPh6IDRa7HCJqRxhgiKhV9fLXoJd/x+pDI6LWxyEkIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHAYYIiIikhwGGCIiIpIcBhgiIiKSHJsHmOeeew4ymazBJTw83Hp7VVUVEhMT4eXlBVdXV0ycOBEFBQUNHiM3NxcJCQlwdnaGj48P5s6di9raWluXSkRERBKlbI0H7d27NzZv3vzHkyj/eJo5c+Zg/fr1WLt2LbRaLWbNmoUJEyZg165dAACTyYSEhAT4+flh9+7duHDhAqZOnQoHBwe88sorrVEuERERSUyrBBilUgk/P7+/XK/X6/HJJ59g1apVuOWWWwAAn332GXr16oU9e/Zg8ODB+PXXX3H06FFs3rwZvr6+6Nu3L1588UXMnz8fzz33HFQqVWuUTERERBLSKj0wJ0+eREBAALp27YrJkycjNzcXAJCeno6amhqMHj3aemx4eDiCg4ORmpoKAEhNTUVkZCR8fX2tx8THx8NgMODIkSPXfE6j0QiDwdDgQkRERO2TzQNMbGwsVqxYgY0bN2LZsmXIycnBTTfdhNLSUuh0OqhUKri7uze4j6+vL3Q6HQBAp9M1CC+W2y23XcvixYuh1Wqtl6CgINu+MCIiIrIbNh9CGjdunPXfUVFRiI2NRUhICNasWQMnJydbP53VggULkJSUZP3ZYDAwxBAREbVTrT6N2t3dHT169MCpU6fg5+eH6upqlJSUNDimoKDA2jPj5+f3l1lJlp+v1ldjoVarodFoGlyIiIiofWr1AFNWVobTp0/D398fMTExcHBwwJYtW6y3Z2dnIzc3F3FxcQCAuLg4ZGVlobCw0HpMcnIyNBoNIiIiWrtcIiIikgCbDyH961//wh133IGQkBDk5+fj2WefhUKhwP333w+tVosZM2YgKSkJnp6e0Gg0ePzxxxEXF4fBgwcDAMaMGYOIiAhMmTIFS5cuhU6nw8KFC5GYmAi1Wm3rcomIiEiCbB5gzp07h/vvvx+XLl1Cp06dMGzYMOzZswedOnUCALz55puQy+WYOHEijEYj4uPj8cEHH1jvr1AosG7dOjz22GOIi4uDi4sLpk2bhhdeeMHWpRIREZFEyQRBEMQuojUYDAZotVro9Xr2wxAREUlEYz+/uRcSERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSY5dB5j3338fXbp0gaOjI2JjY7F3716xSyIiIiI7YLcBZvXq1UhKSsKzzz6LAwcOIDo6GvHx8SgsLBS7NCIiIhKZ3QaYN954A4888ggeeughRERE4MMPP4SzszM+/fRTsUsjIiIikSnFLuBqqqurkZ6ejgULFlivk8vlGD16NFJTU696H6PRCKPRaP1Zr9cDAAwGQ+sWS0RERDZj+dwWBOG6x9llgCkqKoLJZIKvr2+D6319fXH8+PGr3mfx4sV4/vnn/3J9UFBQq9RIRERErae0tBRarfaat9tlgGmOBQsWICkpyfqz2WzG5cuX4eXlBZlMZrPnMRgMCAoKQl5eHjQajc0et6Ph79E2+Hu0Df4ebYO/R9vo6L9HQRBQWlqKgICA6x5nlwHG29sbCoUCBQUFDa4vKCiAn5/fVe+jVquhVqsbXOfu7t5aJUKj0XTIPyxb4+/RNvh7tA3+Hm2Dv0fb6Mi/x+udebGwyyZelUqFmJgYbNmyxXqd2WzGli1bEBcXJ2JlREREZA/s8gwMACQlJWHatGkYMGAABg0ahLfeegvl5eV46KGHxC6NiIiIRGa3Aea+++7DxYsXsWjRIuh0OvTt2xcbN278S2NvW1Or1Xj22Wf/MlxFTcPfo23w92gb/D3aBn+PtsHfY+PIhBvNUyIiIiKyM3bZA0NERER0PQwwREREJDkMMERERCQ5DDBEREQkOQwwTfT++++jS5cucHR0RGxsLPbu3St2SZKyePFiDBw4EG5ubvDx8cH48eORnZ0tdlmSt2TJEshkMsyePVvsUiTn/PnzePDBB+Hl5QUnJydERkZi//79YpclKSaTCc888wxCQ0Ph5OSEbt264cUXX7zhXjYd3Y4dO3DHHXcgICAAMpkMP/zwQ4PbBUHAokWL4O/vDycnJ4wePRonT54Up1g7xADTBKtXr0ZSUhKeffZZHDhwANHR0YiPj0dhYaHYpUlGSkoKEhMTsWfPHiQnJ6OmpgZjxoxBeXm52KVJ1r59+/DRRx8hKipK7FIkp7i4GEOHDoWDgwM2bNiAo0eP4vXXX4eHh4fYpUnKq6++imXLluG9997DsWPH8Oqrr2Lp0qV49913xS7NrpWXlyM6Ohrvv//+VW9funQp3nnnHXz44YdIS0uDi4sL4uPjUVVV1caV2imBGm3QoEFCYmKi9WeTySQEBAQIixcvFrEqaSssLBQACCkpKWKXIkmlpaVC9+7dheTkZOHmm28WnnzySbFLkpT58+cLw4YNE7sMyUtISBCmT5/e4LoJEyYIkydPFqki6QEgfP/999afzWaz4OfnJ7z22mvW60pKSgS1Wi189dVXIlRof3gGppGqq6uRnp6O0aNHW6+Ty+UYPXo0UlNTRaxM2vR6PQDA09NT5EqkKTExEQkJCQ3+LqnxfvrpJwwYMAB/+9vf4OPjg379+uG///2v2GVJzpAhQ7BlyxacOHECAHDo0CHs3LkT48aNE7ky6crJyYFOp2vw3tZqtYiNjeVnTj27XYnX3hQVFcFkMv1lJWBfX18cP35cpKqkzWw2Y/bs2Rg6dCj69OkjdjmS8/XXX+PAgQPYt2+f2KVI1pkzZ7Bs2TIkJSXh3//+N/bt24cnnngCKpUK06ZNE7s8yXj66adhMBgQHh4OhUIBk8mEl19+GZMnTxa7NMnS6XQAcNXPHMttHR0DDIkmMTERhw8fxs6dO8UuRXLy8vLw5JNPIjk5GY6OjmKXI1lmsxkDBgzAK6+8AgDo168fDh8+jA8//JABpgnWrFmDlStXYtWqVejduzcyMjIwe/ZsBAQE8PdIrYZDSI3k7e0NhUKBgoKCBtcXFBTAz89PpKqka9asWVi3bh22bduGwMBAscuRnPT0dBQWFqJ///5QKpVQKpVISUnBO++8A6VSCZPJJHaJkuDv74+IiIgG1/Xq1Qu5ubkiVSRNc+fOxdNPP41JkyYhMjISU6ZMwZw5c7B48WKxS5Msy+cKP3OujQGmkVQqFWJiYrBlyxbrdWazGVu2bEFcXJyIlUmLIAiYNWsWvv/+e2zduhWhoaFilyRJo0aNQlZWFjIyMqyXAQMGYPLkycjIyIBCoRC7REkYOnToX6bxnzhxAiEhISJVJE0VFRWQyxt+nCgUCpjNZpEqkr7Q0FD4+fk1+MwxGAxIS0vjZ049DiE1QVJSEqZNm4YBAwZg0KBBeOutt1BeXo6HHnpI7NIkIzExEatWrcKPP/4INzc361iuVquFk5OTyNVJh5ub21/6hlxcXODl5cV+oiaYM2cOhgwZgldeeQX33nsv9u7di48//hgff/yx2KVJyh133IGXX34ZwcHB6N27Nw4ePIg33ngD06dPF7s0u1ZWVoZTp05Zf87JyUFGRgY8PT0RHByM2bNn46WXXkL37t0RGhqKZ555BgEBARg/frx4RdsTsadBSc27774rBAcHCyqVShg0aJCwZ88esUuSFABXvXz22WdilyZ5nEbdPD///LPQp08fQa1WC+Hh4cLHH38sdkmSYzAYhCeffFIIDg4WHB0dha5duwr/+c9/BKPRKHZpdm3btm1X/f9w2rRpgiDUTaV+5plnBF9fX0GtVgujRo0SsrOzxS3ajsgEgUslEhERkbSwB4aIiIgkhwGGiIiIJIcBhoiIiCSHAYaIiIgkhwGGiIiIJIcBhoiIiCSHAYaIiIgkhwGGiIiIJIcBhoiIiCSHAYaIiIgkhwGGiIiIJIcBhoiIiCTn/wEHtt53vqT4aQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "years = [1, 2, 3]\n", - "quarter = [1, 2, 3, 4]\n", - "data = [1690, 940, 2625, 2500,\n", - " 1800, 900, 2900, 2360,\n", - " 1850, 1100, 2930, 2615]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.ylim(bottom = 0, top = max(data)*1.5)\n", - "plt.plot(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "def create_dummies(data, quarter):\n", - " dummies = np.zeros((len(data), len(quarter)))\n", - "\n", - " for i in range(len(dummies)):\n", - " dummies[i][i%len(quarter)] = 1\n", - "\n", - " return dummies[:, :-1]\n", - "\n", - "\n", - "dummies = create_dummies(data, quarter)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "m = Model()\n", - "\n", - "b_0 = m.continuous_var(name = 'Intercept', lb = -10e6)\n", - "b_x = m.continuous_var_list(len(quarter)-1, lb = -10e6, name = 'Slope')\n", - "\n", - "mse = []\n", - "for i in range(len(data)):\n", - " mse.append((b_0 + b_x @ dummies[i] - data[i])**2)\n", - "\n", - "m.minimize(sum(mse))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 3\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.01 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 4\n", - " Integer space required = 4\n", - " Total non-zeros in factor = 10\n", - " Total FP ops to factor = 30\n", - "Tried aggregator 1 time.\n", - "QP Presolve added 0 rows and 4 columns.\n", - "Reduced QP has 4 rows, 8 columns, and 11 nonzeros.\n", - "Reduced QP objective Q matrix has 4 nonzeros.\n", - "Presolve time = 0.03 sec. (0.00 ticks)\n", - "Parallel mode: using up to 4 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 4\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 4\n", - " Rows in Factor = 4\n", - " Integer space required = 4\n", - " Total non-zeros in factor = 10\n", - " Total FP ops to factor = 30\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 2.0933273e+13 -2.2016119e+13 1.51e-08 0.00e+00 1.17e+07\n", - " 1 9.9958890e+06 -1.1406334e+11 1.42e-08 0.00e+00 6.06e-10\n", - " 2 1.2518012e+05 -5.7015449e+08 4.53e-08 0.00e+00 1.00e-11\n", - " 3 1.2493334e+05 -2.7264625e+06 6.79e-08 0.00e+00 6.10e-12\n", - " 4 1.2493333e+05 1.1067635e+05 2.89e-08 0.00e+00 3.72e-12\n", - " 5 1.2493333e+05 1.2486205e+05 2.91e-08 0.00e+00 5.48e-12\n", - " 6 1.2493333e+05 1.2493298e+05 3.70e-08 0.00e+00 8.42e-12\n", - "Barrier time = 0.07 sec. (0.02 ticks)\n", - "\n", - "Total time on 4 threads = 0.07 sec. (0.02 ticks)\n" - ] - } - ], - "source": [ - "solution = m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "solution for: docplex_model3\n", - "objective: 124933\n", - "status: OPTIMAL_SOLUTION(2)\n", - "Intercept=2491.667\n", - "Slope_0=-711.667\n", - "Slope_1=-1511.667\n", - "Slope_2=326.667\n", - "\n" - ] - } - ], - "source": [ - "print(solution)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/UniversityEnrollment.ipynb b/Time Series/UniversityEnrollment.ipynb deleted file mode 100644 index 68e3658..0000000 --- a/Time Series/UniversityEnrollment.ipynb +++ /dev/null @@ -1,205 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because of high tuition costs at state and private universities,\n", - "enrollments at community colleges have increased dramatically in recent years. The\n", - "following data show the enrollment for Jefferson Community College for the nine most\n", - "recent years:\n", - "\n", - "a. Construct a time series plot. What type of pattern exists in the data?\n", - "\n", - "b. Use simple linear regression analysis to find the parameters for the line that mini-\n", - "mizes MSE for this time series.\n", - "\n", - "c. What is the forecast for year 10?" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "year = [2001,2002,2003,2004,2005,2006,2007,2008,2009 ]\n", - "period = [1,2,3,4,5,6,7,8,9]\n", - "data = [6.5,8.1,8.4,10.2,12.5,13.3,13.7,17.2,18.1]\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.ylim(bottom = 0, top = max(data)*1.5)\n", - "plt.plot(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$eq = B_0 + B_1 * Period$" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from docplex.mp.model import Model\n", - "\n", - "m = Model()\n", - "\n", - "b_0 = m.continuous_var(name = 'Intercept')\n", - "b_1 = m.continuous_var(name = 'Slope')\n", - "\n", - "# for i in period:\n", - "# m.add_constraint(b_0 + b_1 * i == data[i-1])\n", - " \n", - "\n", - "mse = []\n", - "for i in period:\n", - " mse.append(((b_0 + b_1 * i) - data[i-1])**2)\n", - "\n", - "m.minimize(sum(mse))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version identifier: 22.1.1.0 | 2023-02-11 | 22d6266e5\n", - "CPXPARAM_Read_DataCheck 1\n", - "Number of nonzeros in lower triangle of Q = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for factor of Q:\n", - " Rows in Factor = 2\n", - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - "Tried aggregator 1 time.\n", - "QP Presolve added 0 rows and 2 columns.\n", - "Reduced QP has 2 rows, 4 columns, and 5 nonzeros.\n", - "Reduced QP objective Q matrix has 2 nonzeros.\n", - "Presolve time = 0.01 sec. (0.00 ticks)\n", - "Parallel mode: using up to 4 threads for barrier.\n", - "Number of nonzeros in lower triangle of A*A' = 1\n", - "Using Approximate Minimum Degree ordering\n", - "Total time for automatic ordering = 0.00 sec. (0.00 ticks)\n", - "Summary statistics for Cholesky factor:\n", - " Threads = 4\n", - " Rows in Factor = 2\n", - " Integer space required = 2\n", - " Total non-zeros in factor = 3\n", - " Total FP ops to factor = 5\n", - " Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf \n", - " 0 -4.4060000e+01 1.4267400e+03 2.70e+00 0.00e+00 3.73e+03\n", - " 1 -6.7695967e+01 7.8113354e+02 4.35e-01 0.00e+00 6.00e+02\n", - " 2 2.7976023e+00 1.2661955e+02 4.35e-02 0.00e+00 6.00e+01\n", - " 3 4.3568739e+00 -1.0789225e+01 2.66e-15 0.00e+00 6.77e-14\n", - " 4 3.4279666e+00 3.0598962e+00 8.88e-16 0.00e+00 3.18e-14\n", - " 5 3.4273485e+00 3.2436204e+00 4.44e-16 0.00e+00 1.32e-13\n", - " 6 3.4273372e+00 3.3354960e+00 1.33e-15 0.00e+00 4.55e-14\n", - " 7 3.4273343e+00 3.3814195e+00 8.88e-16 0.00e+00 1.99e-14\n", - " 8 3.4273336e+00 3.4043776e+00 1.33e-15 0.00e+00 6.34e-14\n", - " 9 3.4273334e+00 3.4158558e+00 4.44e-16 0.00e+00 1.01e-13\n", - " 10 3.4273333e+00 3.4215946e+00 1.78e-15 0.00e+00 1.07e-13\n", - " 11 3.4273333e+00 3.4244640e+00 0.00e+00 0.00e+00 1.04e-13\n", - " 12 3.4273333e+00 3.4258987e+00 1.33e-15 0.00e+00 2.38e-13\n", - " 13 3.4273333e+00 3.4266160e+00 0.00e+00 0.00e+00 1.03e-13\n", - " 14 3.4273333e+00 3.4269747e+00 4.44e-16 0.00e+00 1.31e-13\n", - " 15 3.4273333e+00 3.4271540e+00 1.78e-15 0.00e+00 1.81e-13\n", - " 16 3.4273333e+00 3.4272437e+00 4.44e-16 0.00e+00 1.09e-13\n", - " 17 3.4273333e+00 3.4272885e+00 1.78e-15 0.00e+00 1.65e-13\n", - " 18 3.4273333e+00 3.4273109e+00 0.00e+00 0.00e+00 1.03e-13\n", - " 19 3.4273333e+00 3.4273221e+00 0.00e+00 0.00e+00 3.26e-14\n", - "Barrier time = 0.06 sec. (0.02 ticks)\n", - "\n", - "Total time on 4 threads = 0.06 sec. (0.02 ticks)\n" - ] - }, - { - "data": { - "text/plain": [ - "docplex.mp.solution.SolveSolution(obj=3.42733,values={Intercept:4.71667,.." - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.solve(log_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "objective: 3.427\n", - "status: OPTIMAL_SOLUTION(2)\n", - " Intercept=4.717\n", - " Slope=1.457\n" - ] - } - ], - "source": [ - "m.print_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Time Series/notes.md b/Time Series/notes.md deleted file mode 100644 index 2183d31..0000000 --- a/Time Series/notes.md +++ /dev/null @@ -1,28 +0,0 @@ -Time series patterns: -1. Horizontal Pattern: The mean is equal over any time period -2. Trend: "Continuous?" Long Term Overall change, whether increasing or decreasing. -3. Seasonal Pattern: The Non-Random behaviour and pattern across a period of time. -4. Trend and Seasonal Pattern: 2+3 -5. ... - -A. Horizontal Pattern: - - Stationary: Statistics are independent from the time variable. - * Plotting a Stationary will always result in a Horizontal with minor changes. - * E.g. Gasoline Problem - -B. Trend Pattern: - * E.g. Bicycle Sales - * E.g. Cholesterol Drug Revenue - -C. Seasonal Pattern: - * E.g. Umbrella Sales - -D. Trend and Seasonal Pattern: - * E.g. Smartphone Sales - -Exponential Smoothing closed form: $\hat{y}_{t+1} = \alpha \sum_{i=0}^{t-2}((1−\alpha)^{i}y_{t-i}) + (1-\alpha)^{t-1}y_1$ - -*** Check Gallery for changing a seasonality to a multivariate problem. Also, in slide 10/19 of chpt 8 -*** CHECK SHADOW PRICES. THEIR FUNCTIONS IN DOCPLEX -*** FORMULATION ==> CHECK HOW TO FORMULATE IN VERSION 0. & HOW TO GENERALIZE BASIC PROBLEMS. -*** WHAT IS CLOSED FORM? OR ITERATIVELY? EXPONENTIAL SMOOTHING EQUATION. \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 1a176d7..0000000 --- a/requirements.txt +++ /dev/null @@ -1,31 +0,0 @@ -asttokens==2.4.1 -colorama==0.4.6 -comm==0.2.1 -cplex==22.1.1.1 -debugpy==1.8.1 -decorator==5.1.1 -docplex==2.25.236 -executing==2.0.1 -ipykernel==6.29.2 -ipython==8.21.0 -jedi==0.19.1 -jupyter_client==8.6.0 -jupyter_core==5.7.1 -matplotlib-inline==0.1.6 -nest-asyncio==1.6.0 -numpy==1.26.4 -packaging==23.2 -parso==0.8.3 -platformdirs==4.2.0 -prompt-toolkit==3.0.43 -psutil==5.9.8 -pure-eval==0.2.2 -Pygments==2.17.2 -python-dateutil==2.8.2 -pywin32==306 -pyzmq==25.1.2 -six==1.16.0 -stack-data==0.6.3 -tornado==6.4 -traitlets==5.14.1 -wcwidth==0.2.13