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

Autor:	Ye, Haoran, Wang, Jiarui, Liang, Helan, Cao, Zhiguang, Li, Yong, Li, Fanzhang
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at AAAI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.08224 Zobrazit plný text záznamu View this record from Arxiv