AI 中文总结
针对大规模供应链的不确定性等挑战，提出结合LLM与优化等的LLM-DE架构，实现更智能、安全、可扩展的供应链决策，为下一代智能供应链提供基础。
AI 中文摘要
当前大规模供应链具有高度不确定性、动态性且易受干扰，传统基于规则和仅优化的系统难以提供及时且有韧性的决策。交易需求信号、非结构化干扰报告等异构数据源的不断增加，为智能系统提供了机会，这类系统可同时进行推理、自适应和优化。本文提出一种将大语言模型（LLM）与数学优化、概率预测及安全约束决策过滤相结合的混合架构，作为决策引擎的实现，名为LLM驱动决策引擎（LLM-DE）。与纯数据驱动或启发式解决方案相比，LLM-DE将LLM的语义推理与一组性能和安全保证相结合，支持大规模供应链流程中的安全决策。该框架支持端到端决策，如需求预测、库存优化、运输路线规划及干扰缓解。研究结果证实，结合优化与形式约束的语言推理不仅能产生更智能，还能更安全、更具可扩展性的供应链决策。本研究提供了一种新架构、完整算法流程及基于LLM的运营决策系统数学构造公式，所提模型为下一代智能供应链基础设施提供了实用和理论基础，可在不确定性与大规模复杂性下可靠运行。
英文摘要
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.
Journal refIC_ASET 2026
DOI：10.1109/IC_ASET69920.2026.11502212
