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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization
TL;DR
RouteRepair is a novel approach for diagnosing and repairing failures in routing heuristics generated by large language models (LLMs). It significantly improves performance on challenging routing problems, reducing optimality gaps and costs.
✦ Why It Matters
Implement RouteRepair to enhance your routing heuristics and improve performance on complex logistics problems today.
Key Takeaways
- Utilize RouteRepair for diagnosing heuristic failures in routing tasks.
- Apply targeted modifications to improve specific routing rules.
- Leverage evidence-based refinements to enhance performance on difficult instances.
- Evaluate your heuristics using matched parent-child performance assessments.
Full Summary
Efficient routing optimization is crucial in logistics and transportation, often requiring high-quality heuristics within limited computational resources. RouteRepair addresses the shortcomings of existing LLM-based heuristic design by diagnosing specific weaknesses in routing rules based on instance-level performance.
It applies targeted modifications to improve these heuristics while maintaining effective behaviors. The methodology combines routing evidence, solver behavior, and program context to set repair objectives, validated through matched evaluations.
In experiments, RouteRepair-GLS reduced the mean optimality gap for the traveling salesman problem (TSP) from 1.7476% to 0.7587%, while the capacitated vehicle routing problem (CVRP) heuristic lowered average route costs by 1.91%. These findings demonstrate that targeted, evidence-based refinements can enhance routing heuristics for difficult instances without degrading performance on easier cases.