  Machine learning has been adapted to help solve NP-hard combinatorial
optimization problems. One prevalent way is learning to construct solutions by
deep neural networks, which has been receiving more and more attention due to
the high efficiency and less requirement for expert knowledge. However, many
neural construction methods for Vehicle Routing Problems~(VRPs) focus on
synthetic problem instances with specified node distributions and limited
scales, leading to poor performance on real-world problems which usually
involve complex and unknown node distributions together with large scales. To
make neural VRP solvers more practical, we design an auxiliary policy that
learns from the local transferable topological features, named local policy,
and integrate it with a typical construction policy (which learns from the
global information of VRP instances) to form an ensemble policy. With joint
training, the aggregated policies perform cooperatively and complementarily to
boost generalization. The experimental results on two well-known benchmarks,
TSPLIB and CVRPLIB, of travelling salesman problem and capacitated VRP show
that the ensemble policy significantly improves both cross-distribution and
cross-scale generalization performance, and even performs well on real-world
problems with several thousand nodes.
