Routing Challenge Dataset Now Publicly Available

by | Sep 14, 2022 | Challenge FAQ & Details

Amazon.com has recently released a publicly accessible copy of the dataset used during the 2021 Last Mile Routing Research Challenge.

This dataset is further described in a short technical article published in Transportation Science.

The technical article can also be used to cite the dataset in any academic publication that wishes to reference the data:

Daniel Merchán, Jatin Arora, Julian Pachon, Karthik Konduri, Matthias Winkenbach, Steven Parks, Joseph Noszek (2022). 2021 Amazon Last Mile Routing Research Challenge: Data Set. Transportation Science 0(0). https://doi.org/10.1287/trsc.2022.1173

Transportation Science special issue. Call for papers – deadline 15 January

by | Nov 23, 2021 | Challenge FAQ & Details

In connection with the “Last-Mile Routing Research Challenge” co-hosted by  Amazon and the MIT Center for Transportation & Logistics in 2021, we are excited to announce a special issue of Transportation Science focused on machine learning-based approaches to large-scale route planning problems. We invite authors independently of whether they took part in the research challenge to submit research papers that address large and complex planning problems related to vehicle routing with nonconventional, data-driven methods. The deadline to submit papers is January 15, 2022

Enter your paper here: https://pubsonline.informs.org/page/trsc/calls-for-papers

Amazon and CTL Announce Winners of Last-Mile Routing Research Challenge

by | Aug 24, 2021 | Challenge FAQ & Details

Winners of the Amazon Last-Mile Routing Research Challenge were announced in a live webinar event held on July 30, 2021. Amazon and the MIT Center for Transportation & Logistics created the challenge to engage with a global community of researchers across a range of disciplines, from computer science to business operations to supply chain management, challenging them to build data-driven route optimization models leveraging massive historical route execution data. The three winning teams were awarded prize money totaling $175,000 for their innovative route optimization models.

Winners ($100,000): 
Professor William Cook, University of Waterloo, Canada; Professor Stephan Held, University of Bonn, Germany; and Professor Emeritus Keld Helsgaun, Roskilde University, Denmark. 

Runners Up ($50,000): 
Xiaotong Guo, Qingyi Wang, and Baichuan Mo, doctoral students at MIT.

3rd Place ($25,000):
Professor Okan Arslan, HEC Montréal; and Rasit Abay, PhD student at the University of New South Wales, Australia.

Read more:

MIT News: Last-mile routing research challenge awards $175,000 to three winning teams

Amazon Science: Winning Last Mile Challenge team addresses problem of combining mathematical routes with driver knowledge

Last-mile routing challenge awards ceremony – webinar event

by | Aug 11, 2021 | Challenge FAQ & Details

Congratulations to all winners and participating teams for such an excellent challenge. Each model addressed the challenge data in a unique way. The methodological approaches that were chosen by the participants frequently combined traditional exact and heuristic optimization approaches with non-traditional machine learning methods.

Watch the award ceremony here.

  • 00:01-11:24 – Setting the context of the research challenge
  • 11:25 – 18:16 – Outline of the collaboration between MIT CTL and Amazon
  • 18:17 – 20:52 – Review of challenge problem set and criteria
  • 20:53 – 25:52 – Overview of participants, locations, and demographics
  • 25:53 – 29:59 – Winners’ announcements and leaderboard
  • 33:00 – 40:25 – Future publications and conclusion

Last-mile routing challenge team performance and leaderboard.

by | Aug 10, 2021 | Challenge FAQ & Details

The research challenge attracted more than two thousand interested participants from around the world. 229 researcher teams formed during the spring of 2021 to independently develop solutions that incorporated driver know-how into route optimization models with the intent that they would outperform traditional optimization approaches. Out of the 48 teams whose models qualified for the final round of the challenge, these teams’ work stood out above the rest.