Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

Abstract

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.

Discipline

OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 40th International Conference on Machine Learning, Honolulu, HI, USA, July 23-29

Volume

202

Publisher

PMLR

City or Country

Honolulu, Hawaii, USA

Copyright Owner and License

Authors

Additional URL

https://proceedings.mlr.press/v202/zhou23o/zhou23o.pdf

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