Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2024

Abstract

The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance on large-scale routing problems, including TSP, ATSP, CVRP, and PCTSP. Our code is available at: https://github.com/henry-yeh/GLOP.

Keywords

PRS, Routing, Combinatorial Optimization, Learning to Search

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 38th AAAI Conference on Artificial Intelligence, AAAI 2024: Vancouver, February 20-27

Volume

38

First Page

20284

Last Page

20292

Identifier

10.1609/aaai.v38i18.30009

Publisher

AAAI Press

City or Country

Vancouver

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1609/aaai.v38i18.30009

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