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

Update Frequency

None

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

Documentation

https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md

Managed By

See all datasets managed by Amazon.

Contact

lastmile-research-challenge@amazon.com

How to Cite

2021 Amazon Last Mile Routing Research Challenge Dataset was accessed on DATE from https://registry.opendata.aws/amazon-last-mile-challenges. Merchan, Daniel; Arora, Jatin; Pachon, Julian; Konduri, Karthik; Winkenbach, Matthias; Parks, Steven; Noszek, Joseph (2022). Transportation Science.

Usage Examples

Tools & Applications

• AWS Last Mile Route Sequence Optimization by Chen Wu, Yin Song, Verdi March, Eden Duthi
  
• 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) by CAVE Lab, MIT Center for Transportation and Logistics
  

Publications

• 2021 Amazon Last Mile Routing Research Challenge: Data Set by Daniel Merchán, Jatin Arora, Julian Pachon, Karthik Konduri, Matthias Winkenbach, Steven Parks, Joseph Noszek
  
• Can language models be used for real-world urban-delivery route optimization? by Yang Liu, Fanyou Wu, Zhiyuan Liu, Kai Wang, Feiyue Wang, Xiaobo Qu
  
• Constrained Local Search for Last-Mile Routing by William Cook, Stephan Held, Keld Helsgaun
  
• Crowdkeeping in Last-Mile Delivery by Xin Wang , Okan Arslan , Erick Delage
  
• Does parking matter? The impact of parking time on last-mile delivery optimization by Sara Reed, Ann Melissa Campbell, Barrett W. Thomas
  
• Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning by Uğur Arikan , Thorsten Kranz, Baris Cem Sal, Severin Schmitt, Jonas Witt
  
• Integrating driver behavior into last-mile delivery routing - Combining machine learning and optimization in a hybrid decision support framework by Peter Dieter, Matthew Caron, Guido Schryen
  
• Inverse Optimization for Routing Problems by Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
  
• Learn global and optimize local A data-driven methodology for last-mile routing by Mayukh Ghosh, Alex Kuiper, Roshan Mahes, Donato Maragno
  
• Machine Learning for Data-Driven Last-Mile Delivery Optimization by Sami Serkan Özarik, Paulo da Costa, Alexandre M. Florio
  
• Making opportunity sales in attended home delivery by Çelen Naz Ötken, Bariş Yildiz, Okan Arslan, Gilbert Laporte
  
• Planning robust drone-truck delivery routes under road traffic uncertainty by Yu Yang, Chiwei Yan, Yufeng Cao, Roberto Roberti
  
• Predicting drivers route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network by Baichuan Mo, Qingyi Wang, Xiaotong Guo, Matthias Winkenbach, Jinhua Zhao
  
• Probability estimation and structured output prediction for learning preferences in last mile delivery by Rocsildes Canoy, Victor Bucarey, Jayanta Mandi, Maxime Mulamba, Yves Molenbruch, Tias Guns
  
• The Driver-Aide Problem Coordinated Logistics for Last-Mile Delivery by S. Raghavan , Rui Zhang