综述神经路由求解器，揭示其启发式本质与评估短板
Survey on Neural Routing Solvers
- 从启发式视角构建分层分类体系，梳理现有方法逻辑
- 提出聚焦泛化能力的新评估流程，发现多个未被察觉的研究缺口
- 适合关注学习型路径优化的算法研究者与工业应用开发者
神经路由求解器（NRSs）利用深度学习解决车辆路径问题，展现出显著的实际应用潜力。通过从数据中学习隐含的启发式规则，NRSs取代了经典启发式框架中的手工设计方法，从而降低对昂贵的人工设计和试错调整的依赖。本综述主要贡献有二：（1）强调NRSs的启发式特性，从启发式角度回顾现有方法，并引入基于启发式原则的分层分类体系；（2）提出一种以泛化能力为导向的评估流程，以弥补传统评估流程的不足。在两种流程下对代表性NRSs进行对比基准测试，揭示了一系列此前未被发现的研究差距。
原文摘要 · Abstract (English)
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.
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