Abstract
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.
Abstract (translated)
这篇论文提出了一种结合传统网络优化模型与大型语言模型（LLM）的综合框架，旨在为供应链规划提供互动、可解释且角色感知型的决策支持。所提出的系统通过生成自然语言摘要、情境化可视化和量身定制的关键绩效指标（KPI），弥合了复杂的运筹学输出与业务利益相关者理解之间的差距。 核心优化模型采用混合整数公式，解决多个周期内多物品在分销中心网络中的战术性库存重新分配问题。技术架构集成了AI代理、RESTful API和动态用户界面，以支持实时交互、配置更新以及基于模拟的洞察分析。 一个案例研究展示了该系统如何通过防止缺货、降低成本和服务水平来改善规划结果。未来的扩展包括整合私有LLM、迁移学习、强化学习和贝叶斯神经网络，从而增强解释性、适应性和实时决策能力。
URL
https://arxiv.org/abs/2508.21622
