Vehicle routing problems (VRPs), which can be found in numerous real-world applications, have been an important research topic for several decades. Recently, the neural combinatorial optimization (NCO) approach that leverages a learning-based model to solve VRPs without manual algorithm design has gained substantial attention. However, current NCO methods typically require building one model for each routing problem, which significantly hinders their practical application for real-world industry problems with diverse attributes. In this work, we make the first attempt to tackle the crucial challenge of cross-problem generalization. In particular, we formulate VRPs as different combinations of a set of shared underlying attributes and solve them simultaneously via a single model through attribute composition. In this way, our proposed model can successfully solve VRPs with unseen attribute combinations in a zero-shot generalization manner. Extensive experiments are conducted on eleven VRP variants, benchmark datasets, and industry logistic scenarios. The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application. The source code is included in https://github.com/FeiLiu36/MTNCO.

翻译：车辆路径问题（VRPs）在众多实际应用中广泛存在，几十年来一直是重要的研究课题。近年来，利用基于学习的模型解决VRPs而无需手动算法设计的神经组合优化（NCO）方法受到了广泛关注。然而，当前的NCO方法通常需要为每个路由问题构建一个模型，这严重阻碍了其在具有多样属性的实际工业问题中的实际应用。本文首次尝试解决跨问题泛化这一关键挑战。具体而言，我们将VRPs形式化为一组共享底层属性的不同组合，并通过属性组合的单一模型同时求解它们。通过这种方式，我们提出的模型能够以零样本泛化方式成功解决具有未见属性组合的VRPs。我们在11个VRP变体、基准数据集和工业物流场景上进行了大量实验。结果表明，统一模型在11个VRP中展现出优越性能，将平均差距从现有方法的超过20%降低至约5%，并在基准数据集以及实际物流应用中实现了显著的性能提升。源代码已开源至https://github.com/FeiLiu36/MTNCO。