  Learning to solve vehicle routing problems (VRPs) has garnered much
attention. However, most neural solvers are only structured and trained
independently on a specific problem, making them less generic and practical. In
this paper, we aim to develop a unified neural solver that can cope with a
range of VRP variants simultaneously. Specifically, we propose a multi-task
vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances
the model capacity without a proportional increase in computation. We further
develop a hierarchical gating mechanism for the MVMoE, delivering a good
trade-off between empirical performance and computational complexity.
Experimentally, our method significantly promotes zero-shot generalization
performance on 10 unseen VRP variants, and showcases decent results on the
few-shot setting and real-world benchmark instances. We further conduct
extensive studies on the effect of MoE configurations in solving VRPs, and
observe the superiority of hierarchical gating when facing out-of-distribution
data. The source code is available at:
https://github.com/RoyalSkye/Routing-MVMoE.
