TY - JOUR
T1 - The first AI4TSP competition
T2 - Learning to solve stochastic routing problems
AU - Zhang, Yingqian
AU - Bliek, Laurens
AU - da Costa, Paulo
AU - Refaei Afshar, Reza
AU - Reijnen, Robbert
AU - Catshoek, Tom
AU - Vos, Daniël
AU - Verwer, Sicco
AU - Schmitt-Ulms, Fynn
AU - Hottung, André
AU - Shah, Tapan
AU - Sellmann, Meinolf
AU - Tierney, Kevin
AU - Perreault-Lafleur, Carl
AU - Leboeuf, Caroline
AU - Bobbio, Federico
AU - Pepin, Justine
AU - Silva, Warley Almeida
AU - Gama, Ricardo
AU - Fernandes, Hugo L.
AU - Zaefferer, Martin
AU - López-Ibáñez, Manuel
AU - Irurozki, Ekhine
N1 - Funding Information:
The organizers would like to thank Ortec and Vanderlande for sponsoring the prize money.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - AI for TSP competition
KW - Deep reinforcement learning
KW - Routing problem
KW - Stochastic combinatorial optimization
KW - Surrogate-based optimization
KW - Travelling salesman problem
UR - http://www.scopus.com/inward/record.url?scp=85152229855&partnerID=8YFLogxK
U2 - 10.1016/j.artint.2023.103918
DO - 10.1016/j.artint.2023.103918
M3 - Article
AN - SCOPUS:85152229855
SN - 0004-3702
VL - 319
JO - Artificial Intelligence
JF - Artificial Intelligence
M1 - 103918
ER -