Real-world Vehicle Routing Problems (RWVRPs) require solving complex, sequence-dependent challenges at scale with constraints such as delivery time window, replenishment or recharging stops, asymmetric travel cost, etc. While recent neural methods achieve strong results on large-scale classical VRP benchmarks, they struggle to address RWVRPs because their strategies overlook sequence dependencies and underutilize edge-level information, which are precisely the characteristics that define the complexity of RWVRPs. We present SEAFormer, a novel transformer that incorporates both node-level and edge-level information in decision-making through two key innovations. First, our Clustered Proximity Attention (CPA) exploits locality-aware clustering to reduce the complexity of attention from $O(n^2)$ to $O(n)$ while preserving global perspective, allowing SEAFormer to efficiently train on large instances. Second, our lightweight edge-aware module captures pairwise features through residual fusion, enabling effective incorporation of edge-based information and faster convergence. Extensive experiments across four RWVRP variants with various scales demonstrate that SEAFormer achieves superior results over state-of-the-art methods. Notably, SEAFormer is the first neural method to solve 1,000+ node RWVRPs effectively, while also achieving superior performance on classic VRPs, making it a versatile solution for both research benchmarks and real-world applications.

翻译：现实世界车辆路径问题（RWVRPs）需要在满足配送时间窗、补给或充电站点、非对称行驶成本等约束条件下，大规模求解复杂的序列依赖型难题。尽管近期基于神经网络的方法在大规模经典VRP基准测试中取得了优异成果，但其策略往往忽视序列依赖性且未充分利用边层级信息，而这正是决定RWVRPs复杂性的关键特征。本文提出SEAFormer——一种通过两项关键创新将节点层级与边层级信息共同纳入决策过程的新型Transformer。首先，我们提出的聚类邻近注意力机制利用局部感知聚类将注意力复杂度从$O(n^2)$降至$O(n)$，同时保持全局视角，使SEAFormer能够高效训练大规模实例。其次，轻量级边缘感知模块通过残差融合捕获成对特征，实现边层级信息的有效整合并加速收敛。在四种不同规模的RWVRP变体上进行的大量实验表明，SEAFormer取得了超越现有最优方法的性能。值得注意的是，SEAFormer是首个能有效求解1000+节点RWVRPs的神经网络方法，同时在经典VRPs上也表现出优越性能，使其成为兼顾研究基准与现实应用场景的通用解决方案。