RouteRepair：基于LLM的自动化启发式设计用于路由优化中的实例级故障诊断与定向修复
RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization
- Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
RouteRepair通过实例级故障诊断和定向修复，在TSP和CVRP上显著提升LLM生成的启发式算法性能，同时不损害已有优势。
AI中文摘要：
高效的路由优化对货运运输、城市物流和共享出行至关重要，在这些场景中，通常需要在有限的计算预算下获得高质量的启发式算法。近期基于大型语言模型（LLM）的自动化启发式设计方法能够生成有效的路由规则，但聚合评估可能掩盖特定实例结构上的反复故障。为解决此局限，本研究开发了RouteRepair，它从实例级性能中诊断父代特定弱点，并对相应的启发式组件进行定向修改，同时保护已表现良好的行为。路由证据、求解器行为和程序上下文被结合以定义有界的修复目标，每次干预通过匹配的父代-子代评估验证故障恢复和附带退化。在旅行商问题（TSP）和带容量约束的车辆路径问题（CVRP）上的实验涵盖了构造式搜索、引导式局部搜索和蚁群优化。RouteRepair-GLS将TSP平均最优性差距从1.7476%降至0.7587%，而构造式CVRP启发式相对于节约启发式将平均路线成本降低了1.91%；生成的ACO先验也优于匹配的手工设计先验。这些结果表明，故障感知、证据约束的细化可以在困难实例上改进路由启发式，同时保持在已能很好解决的案例上的性能。
英文摘要：
Efficient routing optimization is essential to freight transportation, urban logistics, and shared mobility, where high-quality heuristics are often required under limited computational budgets. Recent large language model (LLM)-based automated heuristic design methods can generate effective routing rules, but aggregate evaluation may mask recurrent failures on particular instance structures. To address this limitation, this study develops RouteRepair, which diagnoses parent-specific weaknesses from instance-level performance and applies targeted modifications to the corresponding heuristic components while protecting behavior that already performs well. Routing evidence, solver behavior, and program context are combined to define bounded repair objectives, and each intervention is validated through matched parent-child evaluation of failure recovery and collateral degradation. Experiments on the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) span constructive search, guided local search, and ant colony optimization. RouteRepair-GLS reduces the mean TSP optimality gap from 1.7476% to 0.7587%, while the constructive CVRP heuristic lowers average route cost by 1.91% relative to the savings heuristic; the generated ACO priors also outperform matched hand-designed priors. These results show that failure-aware, evidence-constrained refinement can improve routing heuristics on difficult instances while preserving performance on cases they already solve well.
