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

TL;DR

GLOP introduces a hierarchical neural framework that partitions large routing problems into smaller subproblems, combining different neural heuristics to achieve real-time solutions for large-scale TSP and related problems.

Contribution

It presents a novel hierarchical approach that hybridizes non-autoregressive and autoregressive neural heuristics for scalable, real-time large-scale routing problem solving.

Findings

Achieves state-of-the-art real-time performance on large TSP, ATSP, CVRP, and PCTSP.

Effectively scales to large problems by hierarchical partitioning.

Combines coarse and fine-grained neural heuristics for improved accuracy.

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.