Title: VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Primary area: optimization
Keywords: automated algorithm design, evolutionary search, vehicle routing problem, LLM agent, heuristic discovery

Abstract: Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it still falls short of producing heuristics that rival those crafted by human experts. In this paper, we propose VRPAgent, a framework that integrates LLM-generated components into a metaheuristic and refines them through a novel genetic search. By using the LLM to generate problem-specific operators, embedded within a generic metaheuristic framework, VRPAgent keeps tasks manageable, guarantees correctness, and still enables the discovery of novel and powerful strategies. Across multiple problems, including the capacitated VRP, the VRP with time windows, and the prize-collecting VRP, our method discovers heuristic operators that outperform handcrafted methods and recent learning-based approaches while requiring only a single CPU core. To our knowledge, VRPAgent is the first LLM-based paradigm to advance the state-of-the-art in VRPs, highlighting a promising future for automated heuristics discovery.

TL;DR: We introduce VRPAgent, a framework that leverages LLMs and evolutionary search to discover novel heuristic operators for vehicle routing problems, achieving state-of-the-art performance across multiple VRP variants.