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

• URL: http://arxiv.org/abs/2312.08224v2
• Date: Sun, 21 Jul 2024 10:18:30 GMT
• Title: GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
• Authors: Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li,
• Abstract summary: GLOP is a unified hierarchical framework that efficiently scales toward large-scale routing problems. For the first time, we hybridize non-autoregressive neurals for coarse-grained problem partitions and autoregressive neurals for fine-grained route constructions. Experiments show that GLOP achieves competitive and state-of-the-art real-time performance on large-scale routing problems.
• Score: 17.450373393248984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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