Neural routing solvers (NRSs) that leverage deep learning to tackle vehicle routing problems have demonstrated notable potential for practical applications. By learning implicit heuristic rules from data, NRSs replace the handcrafted counterparts in classic heuristic frameworks, thereby reducing reliance on costly manual design and trial-and-error adjustments. This survey makes two main contributions: (1) The heuristic nature of NRSs is highlighted, and existing NRSs are reviewed from the perspective of heuristics. A hierarchical taxonomy based on heuristic principles is further introduced. (2) A generalization-focused evaluation pipeline is proposed to address limitations of the conventional pipeline. Comparative benchmarking of representative NRSs across both pipelines uncovers a series of previously unreported gaps in current research.

翻译：利用深度学习解决车辆路径问题的神经路由求解器已展现出显著的实际应用潜力。通过从数据中学习隐式启发式规则，NRSs取代了经典启发式框架中手工设计的对应模块，从而降低了对高成本人工设计与试错调整的依赖。本综述作出两项主要贡献：（1）重点阐释了NRSs的启发式本质，并从启发式视角系统评述了现有NRSs，进一步提出了基于启发式原理的层次化分类体系。（2）针对传统评估流程的局限性，提出了以泛化能力为核心的评估框架。通过对代表性NRSs在两种评估流程中的对比基准测试，揭示了当前研究中一系列未被充分报告的认知差距。