The EURO Meets NeurIPS 2022 Vehicle Routing Competition

Wouter Kool, Laurens Bliek, Danilo Numeroso, Yingqian Zhang, Tom Catshoek, Kevin Tierney, Thibaut Vidal, Joaquim Gromicho
Proceedings of the NeurIPS 2022 Competitions Track, PMLR 220:35-49, 2022.

Abstract

Solving vehicle routing problems (VRPs) is an essential task for many industrial applications. Although VRPs have been traditionally studied in the operations research (OR) domain, they have lately been the subject of extensive work in the machine learning (ML) community. Both the OR and ML communities have begun to integrate ML into their methods, but in vastly different ways. While the OR community primarily relies on simplistic ML methods, the ML community generally uses deep learning, but fails to outperform OR baselines. To address this gap, the EURO Meets NeurIPS 2022 Vehicle Routing Competition brought together the OR and ML communities as a joint effort of several previous competitions to solve a challenging VRP variant on real-world data provided by ORTEC, a leading provider of vehicle routing software. The challenge focuses on both a "classic" deterministic VRP with time windows (VRPTW) and a dynamic version in which new orders arrive over the course of a day. Over 50 teams submitted solutions over a 13-week submission period, battling for not only the best performance on the competition problems, but also for the longest dominance of the leaderboard. The goals of the competition were achieved, with both state-of-the-art techniques in OR and ML playing a significant role in several of the winning submissions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v220-kool23a, title = {The EURO Meets NeurIPS 2022 Vehicle Routing Competition}, author = {Kool, Wouter and Bliek, Laurens and Numeroso, Danilo and Zhang, Yingqian and Catshoek, Tom and Tierney, Kevin and Vidal, Thibaut and Gromicho, Joaquim}, booktitle = {Proceedings of the NeurIPS 2022 Competitions Track}, pages = {35--49}, year = {2022}, editor = {Ciccone, Marco and Stolovitzky, Gustavo and Albrecht, Jacob}, volume = {220}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v220/kool23a/kool23a.pdf}, url = {https://proceedings.mlr.press/v220/kool23a.html}, abstract = {Solving vehicle routing problems (VRPs) is an essential task for many industrial applications. Although VRPs have been traditionally studied in the operations research (OR) domain, they have lately been the subject of extensive work in the machine learning (ML) community. Both the OR and ML communities have begun to integrate ML into their methods, but in vastly different ways. While the OR community primarily relies on simplistic ML methods, the ML community generally uses deep learning, but fails to outperform OR baselines. To address this gap, the EURO Meets NeurIPS 2022 Vehicle Routing Competition brought together the OR and ML communities as a joint effort of several previous competitions to solve a challenging VRP variant on real-world data provided by ORTEC, a leading provider of vehicle routing software. The challenge focuses on both a "classic" deterministic VRP with time windows (VRPTW) and a dynamic version in which new orders arrive over the course of a day. Over 50 teams submitted solutions over a 13-week submission period, battling for not only the best performance on the competition problems, but also for the longest dominance of the leaderboard. The goals of the competition were achieved, with both state-of-the-art techniques in OR and ML playing a significant role in several of the winning submissions.} }
Endnote
%0 Conference Paper %T The EURO Meets NeurIPS 2022 Vehicle Routing Competition %A Wouter Kool %A Laurens Bliek %A Danilo Numeroso %A Yingqian Zhang %A Tom Catshoek %A Kevin Tierney %A Thibaut Vidal %A Joaquim Gromicho %B Proceedings of the NeurIPS 2022 Competitions Track %C Proceedings of Machine Learning Research %D 2022 %E Marco Ciccone %E Gustavo Stolovitzky %E Jacob Albrecht %F pmlr-v220-kool23a %I PMLR %P 35--49 %U https://proceedings.mlr.press/v220/kool23a.html %V 220 %X Solving vehicle routing problems (VRPs) is an essential task for many industrial applications. Although VRPs have been traditionally studied in the operations research (OR) domain, they have lately been the subject of extensive work in the machine learning (ML) community. Both the OR and ML communities have begun to integrate ML into their methods, but in vastly different ways. While the OR community primarily relies on simplistic ML methods, the ML community generally uses deep learning, but fails to outperform OR baselines. To address this gap, the EURO Meets NeurIPS 2022 Vehicle Routing Competition brought together the OR and ML communities as a joint effort of several previous competitions to solve a challenging VRP variant on real-world data provided by ORTEC, a leading provider of vehicle routing software. The challenge focuses on both a "classic" deterministic VRP with time windows (VRPTW) and a dynamic version in which new orders arrive over the course of a day. Over 50 teams submitted solutions over a 13-week submission period, battling for not only the best performance on the competition problems, but also for the longest dominance of the leaderboard. The goals of the competition were achieved, with both state-of-the-art techniques in OR and ML playing a significant role in several of the winning submissions.
APA
Kool, W., Bliek, L., Numeroso, D., Zhang, Y., Catshoek, T., Tierney, K., Vidal, T. & Gromicho, J.. (2022). The EURO Meets NeurIPS 2022 Vehicle Routing Competition. Proceedings of the NeurIPS 2022 Competitions Track, in Proceedings of Machine Learning Research 220:35-49 Available from https://proceedings.mlr.press/v220/kool23a.html.

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