用大模型+优化+安全约束，实现可信赖的智能供应链决策
Reliable LLM-Powered Decision Engines for Large-Scale Supply Chain Operations: Architecture, Safety, and Performance Guarantees
- 融合大模型语义推理与数学优化、概率预测和安全过滤
- 端到端支持需求预测、库存优化、运输调度与中断应对
- 兼顾智能性、安全性与可扩展性，适合复杂供应链场景
当前大规模供应链面临高度不确定性、动态变化和易受干扰等问题，传统基于规则或纯优化的系统难以及时提供鲁棒决策。日益丰富的异构数据源（如交易需求信号和非结构化灾情报告）为智能系统提供了机遇，使其能够同时进行推理、适应与优化。本文提出一种混合架构——大语言模型驱动的决策引擎（LLM-DE），将大语言模型（LLMs）与数学优化、概率预报及安全约束决策过滤相结合。相比纯数据驱动或启发式方案，LLM-DE在大规模供应链流程中实现了安全决策，具备性能与安全保证。该框架支持从需求预测、库存优化到运输路径规划和中断缓解的端到端决策。研究证实，结合语言推理、优化与形式化约束，可生成更智能、更安全且更可扩展的供应链决策。本研究提供了新一代智能供应链基础设施的新架构、完整算法流水线及基于数学建模的操作决策系统范式，兼具实践可行性与理论基础。
原文摘要 · Abstract (English)
Current large-scale supply chains are highly uncertain, dynamic, and disruption prone that are challenging to serve up timely and resilient decisions through traditional rule-based and optimization-only systems. The increasing supply of heterogeneous data sources, such as transactional demand signals and unstructured disruption report, presents a chance of intelligent systems, which could reason, adapt and optimize at the same time. A hybrid architecture that combines large language models (LLMs) with mathematical optimization, probabilistic forecasting, and safety-constrained decision filtering is proposed in this paper as a performance of a Decision Engine, which is called LLM-Powered Decision Engine (LLM-DE). In comparison to purely data-driven or heuristic solutions, LLM-DE integrates semantic reasoning with LLM with a set of performance and safety guarantees that allow safe decision-making in large-scale supply chain processes. The suggested framework enables the end-to-end decision making such as demand forecasting, inventory optimization, and transportation routing and disruption mitigation. The findings affirm that language-based reasoning combined with optimization and formal constraints can be used to come up with not only smarter but also safer and more scalable supply chain decisions. This research provides a new architecture, a complete pipeline of algorithm, and a formulation based on mathematical constructs of the operational decision systems incorporating LLM. The proposed model offers a pragmatic and theoretical basis of the next-generation intelligent supply chain infrastructures that can be implemented to work dependably in the face of uncertainty and massive complexity.
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