用智能体框架自动适配零售供应链需求变化，提升系统成功率。
Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations
- 设计图约束智能体框架，联合选择干预路径与模块修改方案。
- 在100个真实仓库需求上，端到端成功率提升至79%-83%。
- 适合需要动态调整的复杂供应链系统研发人员参考。
零售供应链运营依赖耦合决策模块，需随需求演变而适应。大模型提供自然语言接口，但现有方法多聚焦单一优化模型。扩展至异构决策链路时面临挑战：同一需求可能有多种干预路径，且下游影响各异。本文将需求驱动的适应问题建模为干预路径与模块级变更的联合选择，并提出图约束的智能体框架：领域智能体暴露可接受的重构接口，中央处理器在有限路径中搜索最优解，候选方案通过下游关键绩效指标验证与比较。与大型零售伙伴合作，在100个从业务访谈中提取的仓库需求上测试，使用GPT、Qwen和DeepSeek作为基础大模型。相较于直接大模型重构，本框架在所有模型上均提升正确率与端到端成功率，端到端成功率从72%-76%提升至79%-83%。
原文摘要 · Abstract (English)
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%.
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