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大规模供应链运营中可靠的大模型驱动决策引擎：架构、安全与性能保证

文章背景与核心概要

现代大规模供应链面临着高度的不确定性、快速的动态变化以及频繁的供应链中断，这使得传统的基于规则和纯优化系统难以提供及时且具韧性的决策。为了填补这一空白，本文引入了大模型驱动的决策引擎（LLM-Powered Decision Engine, LLM-DE）——一个将大语言模型（LLM）的语义推理能力与数学优化、概率预测以及安全约束决策过滤相结合的混合框架。

通过统一异构数据源（如交易需求信号和非结构化中断报告），LLM-DE 实现了端到端的运营工作流，包括需求预测、库存优化、运输路线规划和中断缓解。该架构保证了更安全、更智能、更具可扩展性的供应链决策，为下一代智能供应链基础设施奠定了务实且理论的基础。

Reliable LLM-Powered Decision Engines for Large-Scale Supply Chain Operations: Architecture, Safety, and Performance Guarantees

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Summary

Modern large-scale supply chains face high uncertainty, rapid dynamics, and frequent disruptions, making traditional rule-based and optimization-only systems inadequate for timely and resilient decision-making. To bridge this gap, this paper introduces the LLM-Powered Decision Engine (LLM-DE)—a hybrid framework that integrates the semantic reasoning capabilities of Large Language Models (LLMs) with mathematical optimization, probabilistic forecasting, and safety-constrained decision filtering.

By unifying heterogeneous data sources (such as transactional demand signals and unstructured disruption reports), LLM-DE enables end-to-end operational workflows, including demand forecasting, inventory optimization, transportation routing, and disruption mitigation. The proposed architecture guarantees safer, smarter, and more scalable supply chain decisions, offering both a pragmatic and theoretical foundation for next-generation intelligent supply chain infrastructures.

Article Details

Abstract

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.

当前的大规模供应链具有高度的不确定性、动态性和易中断性，传统的基于规则和纯优化系统很难针对这些挑战做出及时且具韧性的决策。交易需求信号和非结构化中断报告等异构数据源的不断增多，为构建能够同时进行推理、适应和优化的智能系统提供了契机。

本文提出了一种将大语言模型（LLM）与数学优化、概率预测以及安全约束决策过滤相结合的混合架构，作为一种名为“大模型驱动的决策引擎”（LLM-DE）的决策引擎性能实现。与纯粹的数据驱动或启发式解决方案相比，LLM-DE 将大模型的语义推理与一系列性能和安全保证相结合，从而允许在大规模供应链流程中进行安全决策。所提出的框架支持端到端的决策制定，如需求预测、库存优化、运输路径规划以及中断缓解。

研究结果证实，基于语言的推理与优化及形式化约束相结合，不仅可以做出更智能的供应链决策，还能使其更加安全和可扩展。本研究提供了一种新架构、完整的算法流水线，以及基于结合了 LLM 的运营决策系统数学结构的建模方法。所提出的模型为下一代智能供应链基础设施提供了务实且理论的基础，使其能够在面对不确定性和巨大复杂性时依然可靠运行。