发表机构
Panasonic Connect Co., Ltd.; Kyoto University（松下连接株式会社; 京都大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出多组件构建方法，以隐式封闭车场解释替代逐路线构建，结合Clarke-Wright节省信号，提升神经VRP求解器的全局协调性与鲁棒性。
AI 中文摘要
大多数神经构造式车辆路径问题（VRP）求解器采用逐路线构建方式，即先完整扩展一条路线，再开始下一条。这种方式过早地确定了路线的成员归属，阻碍了跨路线的全局协调。我们提出多组件构建方法，同时维护多个路线组件，并以任意顺序合并它们。这消除了逐路线构建从剩余容量中获得的返回车场线索；为弥补这一点，我们引入一种解释，将每个组件视为隐式封闭车场的路线。在这种封闭车场解释下，标准CVRP构建的每个中间状态都是完整的可行解，合并的精确成本降低即为Clarke-Wright节省量。神经策略将该CW节省信号与演化的组件状态相结合，学习连接什么以及何时连接。仅在CVRP100上训练的策略，在CVRP100-500上使用贪婪推理时，优于代表性神经求解器的报告结果，并在用于破坏与重建时，在高达CVRP1000的所有评估规模上表现强劲。在容量从$C=10$到$500$的零样本约束紧度评估中，它在每种容量下均优于报告的神经求解器。受控分析表明，在没有CW基础的情况下鲁棒性依然存在，并指出学习到的路线闭合行为可能是学习型逐路线求解器在紧约束区域性能下降的合理原因。
英文摘要
Most neural constructive solvers for the vehicle routing problem (VRP) use route-by-route construction, extending one route until completion before starting the next. This commits route membership early and hinders global coordination across routes. We propose multi-component construction, which maintains many route components simultaneously and merges them in an arbitrary order. This removes the depot-return cue that route-by-route construction obtains from the remaining capacity; to compensate, we introduce an interpretation in which every component is treated as an implicitly depot-closed route. Under this depot-closed interpretation, every intermediate state of standard CVRP construction is a complete feasible solution, and the exact cost reduction of a merge is the Clarke-Wright saving. The neural policy combines this CW-saving signal with the evolving component state to learn what to connect and when to connect. A policy trained only on CVRP100 outperforms the reported results of representative neural solvers on CVRP100-500 with greedy inference and, reused for ruin-and-reconstruct, performs strongly at all evaluated sizes up to CVRP1000. In a zero-shot Constraint Tightness evaluation with capacities from $C=10$ to $500$, it outperforms the reported neural solvers at every capacity. Controlled analyses show that robustness persists without CW grounding and point to learned route-closing behavior as a plausible contributor to the tight-regime degradation of learned route-by-route solvers.
