发表机构
Supply Chain Tech Team Y, JD.com（京东集团供应链技术团队Y）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SabreAgent利用设计时语言模型生成季节性先验和策略族，以零运行时调用改进缺货库存控制，在InventoryBench上以0.6311分超越基线并排名第一。
AI 中文摘要
SabreAgent在设计时使用语言模型来构建缺货库存控制的两个组件：一个针对特定产品的季节性先验和一个经过验证选择的封顶基础库存策略族。在运行期间，统计预测和库存优化使用这些冻结的工件来确定订单，零语言模型调用。我们在InventoryBench的1,320个实例上评估了该方法。在基准的成本假设下，运筹学核心借鉴了零提前期最优性结果和针对正确定性提前期的投影库存规则。后者通过沿模拟需求路径传播库存和销售来计算补货短缺。季节性先验在原始预测器之外添加了预测变体，选定的策略族通过订单销毁处理随机提前期。SabreAgent得分为0.6311，而最强已发表基线为0.5380，并在所有六个基准单元中排名第一。消融研究将大部分增益归因于运筹学核心。在配对分析中，季节性组件在三个真实数据单元上增加了1.79%，搜索组件在两个随机提前期单元上增加了2.3%。这些结果展示了模型生成的先验和策略结构如何通过设计时使用改进运筹学控制器。
英文摘要
SabreAgent uses a language model at design time to construct two components for lost-sales inventory control: a product-specific seasonal prior and a validation-selected capped base-stock policy family. During operation, statistical forecasting and inventory optimization use these frozen artifacts to determine orders, with zero language-model calls. We evaluate the approach on the $1{,}320$ instances of InventoryBench. Under the benchmark's cost assumptions, the operations-research core draws on a zero-lead-time optimality result and a projected-inventory rule for positive deterministic lead times. The latter computes replenishment shortfalls by propagating inventory using sales along simulated demand paths. The seasonal prior adds forecast variants alongside the original forecaster, and the selected policy family handles stochastic lead times with order destruction. SabreAgent scores $0.6311$, compared with $0.5380$ for the strongest published baseline, and ranks first in all six benchmark cells. Ablations attribute most of the gain to the OR core. In the paired analysis, the seasonal component adds $1.79\%$ across the three real-data cells, and the search component adds $2.3\%$ across the two stochastic-lead-time cells. These results demonstrate how model-generated priors and policy structure can improve an OR controller through design-time use.
Comments36 pages, 6 figures
