code.py

from __future__ import print_function
from ortools.linear_solver import pywraplp

import numpy as np 
import pandas as pd 


''' 
    Scenario 1. X = 10%, Y = 25% 
    For other scenarios change the below x,y,scen variables 
''' 

x = 0.10
y = 0.25
scen = 'scenario1'

print('X={0}%, Y={1}%, Scenario = {2} \n'.format(np.round(x*100),np.round(y*100),scen))





# Reading and processing the data 
data = pd.read_csv('dataset.csv')
data = data.drop(columns=['Item_id'])
data = data.to_numpy()
data = data.T

R = []
Q = []
H = []

for i in range(5):
  R.append(data[2*i]*data[2*i+1])
  Q.append(data[2*i+1])
  H.append(np.abs(data[2*i]-data[0])*data[2*i+1])

R = np.array(R)
Q = np.array(Q)
H = np.array(H)

base_revenue = np.sum(R[0])
base_quantity = np.sum(Q[0])

R = R.T
Q = Q.T
H = H.T

for i in range(len(R)):
  R[i] = R[i]-R[i][0]
  Q[i] = Q[i]-Q[i][0]

reven = R.flatten()
quan = Q.flatten()
hit = H.flatten()


more_cons = np.zeros((300,1500))


for i in range(300):
  for j in range(5):
    more_cons[i][5*i+j] = 1


more_bnds = np.ones(300)

bounds = []
cons = []

cons.append(reven)
bounds.append(x*base_revenue)
cons.append(quan)
bounds.append(y*base_quantity)

for i in range(300):
  cons.append(more_cons[i])
  bounds.append(more_bnds[i])

tmp = []

def mip_formulation():
  """Preparing the MIP formulation"""
  data = {}
  data['constraint_coeffs'] = cons
  data['bounds'] = bounds
  data['obj_coeffs'] = hit
  data['num_vars'] = 1500
  data['num_constraints'] = 302
  return data


def main():
  data = mip_formulation()
  # Create the mip solver with the CBC backend.
  solver = pywraplp.Solver.CreateSolver('simple_mip_program', 'CBC')
  infinity = solver.infinity()

  # Setting the variables
  x = {}
  for j in range(data['num_vars']):
    x[j] = solver.IntVar(0, 1, 'x[%i]' % j)
  print('Number of variables =', solver.NumVariables())

  # Adding the constraints
  for i in range(data['num_constraints']):
   constraint_expr = [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
   if i<2:
    solver.Add(sum(constraint_expr) >= data['bounds'][i])
   else:
    solver.Add(sum(constraint_expr) == data['bounds'][i])
  print('Number of constraints =', solver.NumConstraints())

  # Defining the objective
  obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]
  solver.Minimize(solver.Sum(obj_expr))

  # Calling the solver
  status = solver.Solve()

  if status == pywraplp.Solver.OPTIMAL:
    print('Final Hit value obtained=', solver.Objective().Value())
    for j in range(data['num_vars']):
      # print(x[j].name(), ' = ', x[j].solution_value())
      tmp.append(x[j].solution_value())
    print()
    print('Problem solved in %f milliseconds' % solver.wall_time())
  else:
    print('The problem does not have an optimal solution.')


if __name__ == '__main__':
  main()

  # Processing the output
  tmp = np.array(tmp)
  sols = []
  for i in range(300):
    for j in range(5):
      if tmp[5*i+j] > 0.5:
        sols.append(j)
        break

  lst = range(1,301)

  # Saving the solution
  df = pd.DataFrame(list(zip(lst,sols)),columns = ['Item_id','Price'])
  df['Price'] = df['Price'].map({0:'Base_Price',1:'Price1',2:'Price2',3:'Price3',4:'Price4'})
  df.to_csv(scen + '.csv',index=False)