Abstract
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 the 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 provide extensive studies on the effect of MoE configurations in solving VRPs. Surprisingly, the hierarchical gating can achieve much better out-of-distribution generalization performance. The source code is available at: this https URL.
Abstract (translated)
学习解决车辆路由问题（VRPs）已经引起了广泛关注。然而，大多数神经网络解决方案只能在特定问题上进行结构化和训练，使它们对其他问题不具有通用性和实用性。在本文中，我们旨在开发一个统一的神经网络解决方案，可以同时处理多种 VRP 变体。具体来说，我们提出了一个多任务车辆路由解决方案（MVMoE），极大地增强了模型能力，而不会增加计算成本。我们进一步开发了一个分层的 gate 机制，使得 MVMoE 可以实现良好的实证性能和计算复杂度的平衡。实验表明，我们的方法在未见过的 10 个 VRP 变体上显著促进了零散样本通用性能，在几见过的设置和现实世界的基准实例上的表现也相当不错。我们还对 MoE 配置对解决 VRP 的影响进行了广泛研究。令人惊讶的是，分层门控可以实现更好的离散样本通用性能。代码可在此处下载：https:// this URL。
URL
https://arxiv.org/abs/2405.01029
