Abstract: With the expansion of electric vehicle ride-hailing fleets,their online dispatching faces stringent real-time requirements and complex decision making.Traditional heuristic rules are widely adopted in industry because they offer both real-time efficiency and interpretability.However,they rely heavily on manual design and adapt poorly to complex dynamic environments.Conventional automatic heuristic design methods,such as genetic programming,are often constrained by predefined modular components and struggle to exploit the semantic information of the problem.Although existing large language model(LLM)-based heuristic design methods exhibit semantic representation capabilities,they lack effective guidance for local optimization,typically requiring multiple rounds of interaction to obtain high-quality rules.To address these issues,an LLM-assisted heuristic rule evolution framework with critic guidance,termed LEHR-C,is proposed.Within the constructed LLM-driven evolutionary search mechanism,only crossover and mutation operators are employed during the LLM-based offspring generation stage to achieve semantic-level global guidance over the design concepts and code structures of the heuristic rules.Furthermore,an LLM-based critic mechanism is introduced to perform defect analysis and provide feedback on candidate rules,thereby offering targeted local optimization directions for rule evolution.Experimental results on real-world road network datasets show that,compared with representative LLM-based automated heuristic design methods,LEHR-C can generate high-quality heuristic rules with strong generalization capabilities whilecutting LLM interactions and inference overhead by approximately 45%.The proposed method effectively improves the efficiency of online dynamic request handling and vehicle route construction,achieving a favorable trade-off between heuristic quality and training cost.

Key words: Electric vehicle, Vehicle routing problem, Online dispatching, Large language model, Automated heuristic design