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The recent end-to-end neural solvers have shown promise for small-scalerouting problems but suffered from limited real-time scaling-up performance.This paper proposes GLOP (Global and Local Optimization Policies), a unifiedhierarchical framework that efficiently scales toward large-scale routingproblems. GLOP partitions large routing problems into Travelling SalesmanProblems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the firsttime, we hybridize non-autoregressive neural heuristics for coarse-grainedproblem partitions and autoregressive neural heuristics for fine-grained routeconstructions, leveraging the scalability of the former and the meticulousnessof the latter. Experimental results show that GLOP achieves competitive andstate-of-the-art real-time performance on large-scale routing problems,including TSP, ATSP, CVRP, and PCTSP.

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