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[2602.23092] Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design
Summary
This paper introduces AILS-AHD, a novel approach that utilizes Large Language Models to enhance the Capacitated Vehicle Routing Problem (CVRP) solver, achieving superior performance in optimization tasks.
Why It Matters
The study addresses the computational challenges of the NP-hard CVRP, a significant problem in operational research. By integrating LLMs into heuristic design, it opens new avenues for improving optimization techniques, potentially benefiting logistics and transportation sectors.
Key Takeaways
- AILS-AHD leverages LLMs for dynamic heuristic generation in CVRP solving.
- The method integrates evolutionary search frameworks to optimize performance.
- Experimental results show AILS-AHD outperforms existing state-of-the-art solvers.
- New best-known solutions were established for 8 out of 10 instances in the CVRPLib benchmark.
- The approach demonstrates the potential of LLMs in enhancing computational efficiency.
Computer Science > Artificial Intelligence arXiv:2602.23092 (cs) [Submitted on 26 Feb 2026] Title:Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design Authors:Zhuoliang Xie, Fei Liu, Zhenkun Wang, Qingfu Zhang View a PDF of the paper titled Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design, by Zhuoliang Xie and 3 other authors View PDF HTML (experimental) Abstract:The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints. While extensively studied in operational research, the NP-hard nature of CVRP continues to pose significant computational challenges, particularly for large-scale instances. This study presents AILS-AHD (Adaptive Iterated Local Search with Automatic Heuristic Design), a novel approach that leverages Large Language Models (LLMs) to revolutionize CVRP solving. Our methodology integrates an evolutionary search framework with LLMs to dynamically generate and optimize ruin heuristics within the AILS method. Additionally, we introduce an LLM-based acceleration mechanism to enhance computational efficiency. Comprehensive experimental evaluations against state-of-the-art solvers, including AILS-II and HGS, demonstrate the superior performance of AILS-AHD across both moderate and large-scale instances. Notably, our approach establishes new best-known solutions for 8 out of 10 instances in the CVRPLib large-scale bench...
