amazon-last-mile-challenges.yaml

Name: 2021 Amazon Last Mile Routing Research Challenge Dataset
Description: "The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in route planning, building on recent advances in predictive modeling, and using a real-world problem and data. The dataset released for the research challenge includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world dataset excludes any personally identifiable information (all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity). Although multiple synthetic benchmark datasets are available in the literature, the dataset of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available dataset to include instances based on real-world operational routing data. The dataset is fully described and formally introduced in the following Transportation Science article: https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173" 


Documentation: https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md
Contact: lastmile-research-challenge@amazon.com
ManagedBy: "[Amazon](https://www.amazon.com/)"
UpdateFrequency: None

Tags:
  - transportation
  - machine learning
  - deep learning
  - amazon.science
  - urban
  - analytics
  - geospatial
  - logistics
  - last mile
  - optimization
  - routing

License: "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The material for the Amazon Last Mile Routing Research Challenge is provided under a Creative Commons Attribution-NonCommercial 4.0 International Public License (the “License”). You may not use this material except in compliance with the License. You may obtain a copy of the License at: https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt"

Citation: "Merchan, Daniel; Arora, Jatin; Pachon, Julian; Konduri, Karthik; Winkenbach, Matthias; Parks, Steven; Noszek, Joseph (2022). Transportation Science."

Resources:
  - Description: "Dataset including training and testing data. Folder almrrc2021_data_training includes 6,112 historical routes used for model traning; folder almrrc2021_data_evaluation includes 3072 historical routes used for model evaluation. All routes correspond to year 2018. Please refer to the Tranportation Science article  (https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173) for a comprehensive description of the dataset, and to this documentation page (https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md) for details on data structure and format."
    ARN: arn:aws:s3:::amazon-last-mile-challenges
    Region: us-west-2
    Type: S3 Bucket
DataAtWork:
  Tools & Applications:
    - Title: Code repository used for the 2021 Amazon Routing Research Challenge (this repository is included for reference and documentation purposes only, you do not need to install it to access the data)
      URL: https://github.com/MIT-CAVE/rc-cli
      AuthorName: CAVE Lab, MIT Center for Transportation and Logistics
      AuthorURL: https://cave.mit.edu/
    - Title: AWS Last Mile Route Sequence Optimization
      URL: https://github.com/aws-samples/amazon-sagemaker-amazon-routing-challenge-sol
      AuthorName: Chen Wu, Yin Song, Verdi March, Eden Duthi
  Publications:
    - Title: "2021 Amazon Last Mile Routing Research Challenge: Data Set"
      URL: https://pubsonline.informs.org/doi/pdf/10.1287/trsc.2022.1173
      AuthorName: Daniel Merchán, Jatin Arora, Julian Pachon, Karthik Konduri, Matthias Winkenbach, Steven Parks, Joseph Noszek
    - Title: Can language models be used for real-world urban-delivery route optimization?
      URL: https://www.cell.com/the-innovation/pdf/S2666-6758(23)00148-0.pdf
      AuthorName: Yang Liu, Fanyou Wu, Zhiyuan Liu, Kai Wang, Feiyue Wang, Xiaobo Qu
    - Title: Constrained Local Search for Last-Mile Routing
      URL: https://doi.org/10.1287/trsc.2022.1185
      AuthorName: William Cook, Stephan Held, Keld Helsgaun
    - Title: Predicting drivers route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network
      URL: https://doi.org/10.1016/j.tre.2023.103168
      AuthorName: Baichuan Mo, Qingyi Wang, Xiaotong Guo, Matthias Winkenbach, Jinhua Zhao
    - Title: Integrating driver behavior into last-mile delivery routing - Combining machine learning and optimization in a hybrid decision support framework
      URL: https://doi.org/10.1016/j.ejor.2023.04.043
      AuthorName: Peter Dieter, Matthew Caron, Guido Schryen
    - Title: Machine Learning for Data-Driven Last-Mile Delivery Optimization
      URL: https://doi.org/10.1287/trsc.2022.0029
      AuthorName: Sami Serkan Özarik, Paulo da Costa, Alexandre M. Florio
    - Title: Making opportunity sales in attended home delivery
      URL: https://doi.org/10.1016/j.cor.2023.106362
      AuthorName: Çelen Naz Ötken, Bariş Yildiz, Okan Arslan, Gilbert Laporte
    - Title: Planning robust drone-truck delivery routes under road traffic uncertainty
      URL: https://doi.org/10.1016/j.ejor.2023.02.031
      AuthorName: Yu Yang, Chiwei Yan, Yufeng Cao, Roberto Roberti
    - Title: The Driver-Aide Problem Coordinated Logistics for Last-Mile Delivery
      URL: https://doi.org/10.1287/msom.2022.0211
      AuthorName: S. Raghavan , Rui Zhang
    - Title: Inverse Optimization for Routing Problems
      URL: https://arxiv.org/abs/2307.07357
      AuthorName: Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
    - Title: Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning
      URL: https://doi.org/10.1287/inte.2023.0031
      AuthorName: Uğur Arikan , Thorsten Kranz, Baris Cem Sal, Severin Schmitt, Jonas Witt 
    - Title: Crowdkeeping in Last-Mile Delivery
      URL: https://doi.org/10.1287/trsc.2022.0323
      AuthorName: Xin Wang , Okan Arslan , Erick Delage
    - Title: Learn global and optimize local A data-driven methodology for last-mile routing
      URL: https://doi.org/10.1016/j.cor.2023.106312
      AuthorName: Mayukh Ghosh, Alex Kuiper, Roshan Mahes, Donato Maragno
    - Title: Probability estimation and structured output prediction for learning preferences in last mile delivery
      URL: https://doi.org/10.1016/j.cie.2024.109932
      AuthorName: Rocsildes Canoy, Victor Bucarey, Jayanta Mandi, Maxime Mulamba, Yves Molenbruch, Tias Guns
    - Title: Does parking matter? The impact of parking time on last-mile delivery optimization
      URL: https://doi.org/10.1016/j.tre.2023.103391
      AuthorName: Sara Reed, Ann Melissa Campbell, Barrett W. Thomas