用大模型让供应链规划更智能、可解释且能互动。
Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study
- 结合优化模型与大模型，生成自然语言摘要和可视化
- 多周期多品类库存重分配，降低缺货率并节省成本
- 适合需要透明决策的供应链管理者使用
本文提出一个融合传统网络优化模型与大语言模型（LLMs）的集成框架，为供应链规划提供交互式、可解释且角色感知的决策支持。该系统通过生成自然语言摘要、上下文可视化及定制化关键绩效指标（KPI），弥合复杂运筹学结果与业务人员理解之间的差距。核心优化模型采用混合整数规划方法，解决多周期、多品类的分销中心库存再分配问题。技术架构包含AI代理、RESTful API和动态用户界面，支持实时交互、配置更新与基于仿真的洞察。案例研究显示，该系统有效防止缺货、降低成本并维持服务水平。未来扩展方向包括集成私有LLMs、迁移学习、强化学习和贝叶斯神经网络，以提升可解释性、适应性和实时决策能力。
原文摘要 · Abstract (English)
This paper presents an integrated framework that combines traditional network optimization models with large language models (LLMs) to deliver interactive, explainable, and role-aware decision support for supply chain planning. The proposed system bridges the gap between complex operations research outputs and business stakeholder understanding by generating natural language summaries, contextual visualizations, and tailored key performance indicators (KPIs). The core optimization model addresses tactical inventory redistribution across a network of distribution centers for multi-period and multi-item, using a mixed-integer formulation. The technical architecture incorporates AI agents, RESTful APIs, and a dynamic user interface to support real-time interaction, configuration updates, and simulation-based insights. A case study demonstrates how the system improves planning outcomes by preventing stockouts, reducing costs, and maintaining service levels. Future extensions include integrating private LLMs, transfer learning, reinforcement learning, and Bayesian neural networks to enhance explainability, adaptability, and real-time decision-making.
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