GLOP: Learning global partition and local construction for solving large-scale routing problems in real-time

• Main Authors: YE, Haoran, WANG, Jiarui, LIANG, Helan, CAO, Zhiguang, LI, Yong, LI, Fanzhang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: PRS; Routing; Combinatorial Optimization; Learning to Search; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8732; https://ink.library.smu.edu.sg/context/sis_research/article/9735/viewcontent/30009_Article_Text_34063_1_2_20240324_pvoa.pdf
• Institution: Singapore Management University

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.