The first AI4TSP competition

Learning to solve stochastic routing problems

Journal article (2023)

Authors

Yingqian Zhang Eindhoven University of Technology
Laurens Bliek Eindhoven University of Technology
Paulo da Costa Eindhoven University of Technology
Reza Refaei Afshar Eindhoven University of Technology
Robbert Reijnen Eindhoven University of Technology
Fynn Schmitt-Ulms McGill University
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Copyright: © 2023 Yingqian Zhang, Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Robbert Reijnen, T. Catshoek, D.A. Vos, S.E. Verwer, Fynn Schmitt-Ulms, More Authors
DOI: https://doi.org/10.1016/j.artint.2023.103918
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Published Date

2023

Language

English

Abstract

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the participants to develop algorithms to solve an orienteering problem with stochastic weights and time windows (OPSWTW). It focused on two learning approaches: surrogate-based optimization and deep reinforcement learning. In this paper, we describe the problem, the competition setup, and the winning methods, and give an overview of the results. The winning methods described in this work have advanced the state-of-the-art in using AI for stochastic routing problems. Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers. The simulator of the problem has been made open-source and can be used by other researchers as a benchmark for new learning-based methods. The instances and code for the competition are available at https://github.com/paulorocosta/ai-for-tsp-competition.