适应不断变化的需求：面向零售供应链运营的智能体AI

中文摘要 AI 辅助

零售供应链运营依赖耦合的决策模块，这些模块必须随需求的演变而调整。大语言模型（LLM）为该任务提供了自然语言接口，但现有方法主要聚焦于单个优化模型。将其扩展到异构决策流程颇具挑战性，因为一项需求可能存在多种干预路径，且不同路径会产生不同的下游影响。我们将需求驱动的适应问题形式化为干预路径与可允许模块级变更的联合选择，提出一种图约束的智能体框架，其中领域智能体暴露可允许的重构接口，中央处理器则在有界干预路径中进行搜索。候选方案通过下游关键绩效指标（KPI）进行验证与比较。我们与一家大型零售合作伙伴合作，评估了从从业者访谈中获取的100项仓库需求，采用GPT、Qwen和DeepSeek作为基础大语言模型。与直接的大语言模型重构相比，我们的框架在所有三个模型上均提升了正确性与端到端成功率，使端到端成功率从72%-76%提升至79%-83%。

英文摘要

Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging because a requirement may admit multiple intervention paths with different downstream effects. We formulate requirement-driven adaptation as the joint selection of an intervention route and an admissible module-level change, and propose a graph-constrained agentic framework in which domain agents expose admissible reformulation interfaces and a central processor searches over bounded intervention paths. Candidates are validated and compared using downstream KPIs. In collaboration with a large retail partner, we evaluate 100 warehouse requirements elicited from practitioner interviews, with GPT, Qwen, and DeepSeek as base LLMs. Relative to direct LLM reformulation, our framework improves correctness and end-to-end success across all three models, raising end-to-end success from 72--76% to 79--83%.

发表机构

• School of Business and Management, Hong Kong University of Science and Technology（香港科技大学工商管理学院）
• School of Management, University of Science and Technology of China（中国科学技术大学管理学院）
• Institute of Operations Research and Analytics, National University of Singapore（新加坡国立大学运筹学与分析研究所）
• NUS Business School, National University of Singapore（新加坡国立大学商学院）

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