Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

TL;DR

This paper introduces AILS-AHD, an innovative CVRP solver that uses Large Language Models to automatically generate and optimize heuristics, significantly improving solution quality and efficiency for large-scale instances.

Contribution

It presents a novel LLM-driven heuristic design integrated with an evolutionary search framework, advancing CVRP solving methods.

Findings

Achieved new best-known solutions for 8 out of 10 benchmark instances.

Demonstrated superior performance over state-of-the-art solvers like AILS-II and HGS.

Enhanced computational efficiency through LLM-based acceleration mechanisms.

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 benchmark, underscoring the potential of LLM-driven heuristic design in advancing the field of vehicle routing optimization.