This study investigates large language model (LLM) -based multi-agent systems (MASs) as a promising approach to inventory management, which is a key component of supply chain management. Although these systems have gained considerable attention for their potential to address the challenges associated with typical inventory management methods, key uncertainties regarding their effectiveness persist. Specifically, it is unclear whether LLM-based MASs can consistently derive optimal ordering policies and adapt to diverse supply chain scenarios. To address these questions, we examine an LLM-based MAS with a fixed-ordering strategy prompt that encodes the stepwise processes of the problem setting and a safe-stock strategy commonly used in inventory management. Our empirical results demonstrate that, even without detailed prompt adjustments, an LLM-based MAS can determine optimal ordering decisions in a restricted scenario. To enhance adaptability, we propose a novel agent called AIM-RM, which leverages similar historical experiences through similarity matching. Our results show that AIM-RM outperforms benchmark methods across various supply chain scenarios, highlighting its robustness and adaptability.

翻译：本研究探讨了基于大语言模型（LLM）的多智能体系统（MAS）作为一种有前景的库存管理方法，而库存管理是供应链管理的关键组成部分。尽管这些系统因其解决典型库存管理方法相关挑战的潜力而受到广泛关注，但其有效性的关键不确定性仍然存在。具体而言，目前尚不清楚基于LLM的MAS能否持续推导出最优订购策略并适应多样化的供应链场景。为解答这些问题，我们研究了一个基于LLM的MAS，该系统采用固定订购策略提示，该提示编码了问题设置的逐步流程以及库存管理中常用的安全库存策略。我们的实证结果表明，即使不进行详细的提示调整，基于LLM的MAS也能在受限场景中确定最优订购决策。为增强适应性，我们提出了一种名为AIM-RM的新型智能体，它通过相似性匹配利用相似的历史经验。我们的结果显示，AIM-RM在各种供应链场景中均优于基准方法，突显了其鲁棒性和适应性。