Learning to Delegate for Large-scale Vehicle Routing

• URL: http://arxiv.org/abs/2107.04139v1
• Date: Thu, 8 Jul 2021 22:51:58 GMT
• Title: Learning to Delegate for Large-scale Vehicle Routing
• Authors: Sirui Li, Zhongxia Yan, Cathy Wu
• Abstract summary: Vehicle routing problems (VRPs) are a class of problems with wide practical applications. Previous or learning-based works achieve decent solutions on small problem instances of up to 100 customers. This article presents a novel learning-augmented local search algorithm to solve large-scale VRP.
• Score: 4.425982186154401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle routing problems (VRPs) are a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions on small problem instances of up to 100 customers, their performance does not scale to large problems. This article presents a novel learning-augmented local search algorithm to solve large-scale VRP. The method iteratively improves the solution by identifying appropriate subproblems and $\textit{delegating}$ their improvement to a black box subsolver. At each step, we leverage spatial locality to consider only a linear number of subproblems, rather than exponential. We frame subproblem selection as a regression problem and train a Transformer on a generated training set of problem instances. We show that our method achieves state-of-the-art performance, with a speed-up of up to 15 times over strong baselines, on VRPs with sizes ranging from 500 to 3000.

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