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Machine learning has been adapted to help solve NP-hard combinatorialoptimization problems. One prevalent way is learning to construct solutions bydeep neural networks, which has been receiving more and more attention due tothe high efficiency and less requirement for expert knowledge. However, manyneural construction methods for Vehicle Routing Problems (VRPs) focus onsynthetic problem instances with specified node distributions and limitedscales, leading to poor performance on real-world problems which usuallyinvolve complex and unknown node distributions together with large scales. Tomake neural VRP solvers more practical, we design an auxiliary policy thatlearns from the local transferable topological features, named local policy,and integrate it with a typical construction policy (which learns from theglobal information of VRP instances) to form an ensemble policy. With jointtraining, the aggregated policies perform cooperatively and complementarily toboost generalization. The experimental results on two well-known benchmarks,TSPLIB and CVRPLIB, of travelling salesman problem and capacitated VRP showthat the ensemble policy significantly improves both cross-distribution andcross-scale generalization performance, and even performs well on real-worldproblems with several thousand nodes.

Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy