利用大语言模型自动设计库存策略：一项探索性研究

中文摘要 AI 辅助

做出库存决策的企业可以获取运营数据、优化工具和大语言模型（LLMs）。通常，数据描述运营环境，优化在预先指定的库存策略类别中选择参数，而LLMs支持编码和决策分析。我们开发了一个集成框架，结合这些资源以实现库存策略设计的自动化。给定需求数据，该框架迭代地使用LLM生成参数化策略类别，并使用外部求解器优化每个类别中的参数。在30个缺货销售库存实例中，相对于优化的基准库存基准，平均成本降低从第一代后的17.5%增加到第十代后的30.0%。参数优化是这一性能的核心：仅使用LLM的变体表现明显较差，而优化引导的反馈提高了策略质量，加速了搜索，并引导LLM朝向更好的策略类别，而不仅仅是在固定类别中寻找更好的参数值。发现的最强策略也是可解释的：它们结合了可识别的库存控制模式，包括有上限的订单、折扣或加权的在途库存，以及基于阈值的补货逻辑。因此，搜索产生了新的策略类别函数形式，据我们所知，这些形式此前在缺货销售库存文献中未被研究过。这些函数形式并非事先指定，而是从搜索过程中涌现出来的。此外，在重新优化其参数后，三个发现的策略类别在10,064个新库存实例中实现了21.75%至22.60%的平均成本降低。总体而言，结果表明，数据驱动的参数优化可以引导基于LLM的搜索在广泛的库存策略类别空间中进行，并识别出高性能、可解释且可迁移的决策规则。

英文摘要

Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the operating environment, optimization selects parameters within a prespecified inventory policy class, and LLMs support coding and decision analysis. We develop an integrated framework that combines these resources to automate inventory policy design. Given demand data, the framework iteratively uses an LLM to generate parameterized policy classes and an external solver to optimize its parameters within each class. Across 30 lost-sales inventory instances, the mean cost reduction relative to optimized base-stock benchmarks increases from 17.5% after one generation to 30.0% after ten generations. Parameter optimization is central to this performance: an LLM-only variant performs substantially worse, whereas optimization-guided feedback improves policy quality, accelerates search, and directs the LLM toward better policy classes rather than merely better parameter values within a fixed class. The strongest discovered policies are also interpretable: they combine recognizable inventory-control motifs, including capped orders, discounted or weighted pipeline inventory, and threshold-based replenishment logic. The search thereby produces new policy-class functional forms that, to our knowledge, have not previously been studied in the lost-sales inventory literature. These functional forms are not specified ex ante but emerge from the search process. Moreover, after their parameters are re-optimized, three discovered policy classes achieve average cost reductions of 21.75% to 22.60% across 10,064 new inventory instances. Overall, the results show that data-driven parameter optimization can guide LLM-based search over a broad space of inventory policy classes and identify high-performing, interpretable, and transferable decision rules.

发表机构

• University of Michigan（密歇根大学）
• Stanford University（斯坦福大学）
• University at Buffalo, State University of New York（纽约州立大学布法罗分校）

机构由 AI 辅助整理，请以论文原文为准。