MVMoE：基于专家混合的多任务车辆路径求解器
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
AI总结：
本文提出MVMoE多任务车辆路径求解器，通过专家混合与分层门控机制在控制计算成本的同时提升模型容量，并在多个VRP变体上取得优异零样本泛化性能。
摘要：
学习求解车辆路径问题（VRPs）已受到广泛关注。然而，大多数神经求解器仅针对特定问题独立构建和训练，使其通用性和实用性较差。本文旨在开发一种统一的神经求解器，能够同时应对多种VRP变体。具体而言，我们提出了一种基于专家混合的多任务车辆路径求解器（MVMoE），它在不按比例增加计算量的情况下大幅提升了模型容量。我们进一步为MVMoE开发了一种分层门控机制，在经验性能与计算复杂度之间实现了良好的权衡。实验上，我们的方法在10个未见过的VRP变体上显著提升了零样本泛化性能，并在少样本设置和真实世界基准实例上展示了不错的结果。我们还对MoE配置在求解VRP中的效果进行了广泛研究，并观察到分层门控在面对分布外数据时的优越性。源代码可在以下地址获取：https://github.com/RoyalSkye/Routing-MVMoE。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
