Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design
This work addresses the computational challenges of large-scale CVRP instances for logistics and operational research by improving solution quality, representing a strong specific gain for this domain.
This study introduces AILS-AHD, an approach that uses Large Language Models (LLMs) within an evolutionary search framework to automatically design and optimize ruin heuristics for the Capacitated Vehicle Routing Problem (CVRP). It establishes new best-known solutions for 8 out of 10 instances in the CVRPLib large-scale benchmark.
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 benchmark, underscoring the potential of LLM-driven heuristic design in advancing the field of vehicle routing optimization.