Leveraging large language models to enhance multi-agent risk assessment in supply chain networks
提出一个基于大语言模型的多智能体框架MARS，通过整合结构化与非结构化因素（如极端天气事件文本），评估物流枢纽选址风险，并以美国东南部为测试案例。
We propose a novel large language model (LLM) enhanced framework, MARS (Multi-Agent Risk assessment in Supply chain networks), for risk assessment and integration of both structured and unstructured factors for logistic hub site selection across a target territory, using the southeastern U.S. states as a testbed. While structured factors such as cost, distance, traffic accidents, traffic congestion, and crime rates can be directly computed, unstructured severe weather event narratives need to be interpreted semantically through LLMs. We introduce a multi-agent architecture featuring three specialised agents, RiskAgent, FeedbackAgent, and RevisionAgent, that collaborate through a feedback-revision loop to convert raw extreme weather event narratives into fine-grained risk severity levels. By integrating these severity levels with structured indicators via aggregation, the proposed method enables interpretable and risk-aware ranking of candidate hubs, thereby supporting informed decision-making for logistic hub site selection.