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.

提出了 GLOP（Global and Local Optimization Policies）方法，该方法使用分层框架有效扩展到大规模路由问题，同时利用导数和非导数神经启发式算法以实现高性能和高可拓展性。