用AI代理系统自动优化库存决策，适应多种供应链场景。
AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval
- 设计固定订单策略提示，让大模型理解库存问题步骤。
- 在限定场景下，无需调参也能得出最优订货方案。
- 新代理AIM-RM通过历史经验匹配提升适应性，效果更优。
本研究探讨基于大语言模型（LLM）的多智能体系统（MAS）在库存管理中的应用，这是供应链管理的关键环节。尽管这类系统因有望解决传统方法的挑战而受到关注，但其有效性仍存在不确定性，尤其是能否持续制定最优订货策略并适应多样供应链场景。为此，我们考察了一个采用固定订货策略提示的LLM-MAS，该提示编码了问题设定的分步流程及库存管理中常见的安全库存策略。实证结果表明，即使不进行详细提示调整，该系统在受限场景下仍可确定最优订货决策。为增强适应性，我们提出新型智能体AIM-RM，通过相似性匹配复用历史经验。结果显示，AIM-RM在多种供应链场景中均优于基准方法，凸显其鲁棒性与适应能力。
原文摘要 · Abstract (English)
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.
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