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RouteRepair：面向路由优化的基于LLM自动启发式设计中的实例级失败诊断与定向修复
该论文针对 LLM 自动启发式设计中聚合评估掩盖特定实例结构上反复失败的问题，提出 RouteRepair：从实例级性能诊断父代弱点，对相应启发式组件做定向修改并保护已表现良好的行为，结合路由证据、求解器行为与程序上下文定义有界修复目标，并以匹配的父子评估验证失败恢复与附带退化。在 TSP 和 CVRP 上覆盖构造式搜索、引导局部搜索与蚁群优化：RouteRepair-GLS 将 TSP 平均最优性差距从 1.7476% 降至 0.7587%，构造式 CVRP 启发式相对 savings 启发式平均路径成本降低 1.91%，生成的 ACO 先验优于匹配的人工设计先验。
AI推荐理由 arXiv 新提交论文（2026-09-10），提出 RouteRepair：从实例级性能诊断父代启发式的特定弱点，并对相应启发式组件做定向修改，同时保护已表现良好的行为；用路由证据、求解器行为与程序上下文定义有界修复目标，并通过匹配的父子评估验证失败恢复与附带退化。在 TSP 与 CVRP 上覆盖构造式搜索、引导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%，构造式 CVRP 启发式相对 savings 启发式平均路径成本降低 1.91%，生成的 ACO 先验也优于匹配的人工设计先验。局限是仅摘要级证据、未提供代码或复现细节，且结果限于 TSP/CVRP 两类问题。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.11452 (cs)
[Submitted on 10 Sep 2026]
Title:RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization
Authors:Binghao Ji, Di Huang, Jiahui Fang, Zhiyuan Liu
View a PDF of the paper titled RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization, by Binghao Ji and 3 other authors
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Abstract: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.
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Artificial Intelligence (cs.AI)
Cite as:
arXiv:2609.11452 [cs.AI]
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arXiv:2609.11452v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.11452
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arXiv-issued DOI via DataCite (pending registration)
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From: Di Huang [view email]
[v1]
Thu, 10 Sep 2026 12:21:18 UTC (2,706 KB)
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