  Learning heuristics for vehicle routing problems (VRPs) has gained much
attention due to the less reliance on hand-crafted rules. However, existing
methods are typically trained and tested on the same task with a fixed size and
distribution (of nodes), and hence suffer from limited generalization
performance. This paper studies a challenging yet realistic setting, which
considers generalization across both size and distribution in VRPs. We propose
a generic meta-learning framework, which enables effective training of an
initialized model with the capability of fast adaptation to new tasks during
inference. We further develop a simple yet efficient approximation method to
reduce the training overhead. Extensive experiments on both synthetic and
benchmark instances of the traveling salesman problem (TSP) and capacitated
vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The
code is available at: https://github.com/RoyalSkye/Omni-VRP.
