The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems

TL;DR

This paper presents the first AI4TSP competition focused on solving stochastic routing problems with learning-based methods, highlighting advancements in AI approaches for complex, real-world routing challenges and providing an open-source benchmark.

Contribution

It introduces a new competition on stochastic routing problems, details the winning AI methods, and provides an open-source simulator for future research.

Findings

Winning methods advanced AI for stochastic routing

Open-source simulator available for benchmarking

The competition fostered AI research in complex routing problems

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 a time-dependent orienteering problem with stochastic weights and time windows (TD-OPSWTW). It focused on two types of learning approaches: surrogate-based optimization and deep reinforcement learning. In this paper, we describe the problem, the setup of the competition, 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 AI methods.