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.
@article{arxiv.2312.08224,
title = {GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time},
author = {Haoran Ye and Jiarui Wang and Helan Liang and Zhiguang Cao and Yong Li and Fanzhang Li},
journal= {arXiv preprint arXiv:2312.08224},
year = {2024}
}