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arXiv · 2312.08224

GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time

Haoran Ye·Jiarui Wang·Helan Liang·Zhiguang Cao·Yong Li·Fanzhang Li
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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.

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Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li. 2023-12-13. GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time. https://arxiv.org/abs/2312.08224

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