Prescriptive Analysis for Perishable One-Warehouse, Multi-Retailer Systems with Contextual Information

Motivated by the perishable inventory system of JD in Shanghai, we establish a two-echelon perishable One-Warehouse Multi-Retailer (OWMR) inventory system. A key challenge in such systems lies in determining optimal allocations with unknown demand distributions faced by each retailer. Unlike most existing studies that rely on prior assumptions about underlying distribution, we propose a data-driven distribution-free prescriptive analysis framework—Constrained Local Learning (CLL) model framework leveraging historical dataset to estimate unknown demand distribution accurately. This novel framework uses integrated machine learning methods to prescribe ordering quantity for each retailer directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying distribution. To demonstrate the effectiveness of our framework, we theoretically analyze its properties, including asymptotic optimality, convexity and establish performance guarantee. We design a tractable WFS-OPT algorithm for solving the model framework and numerically compare the performance of different weight functions under varying scenarios. Compared to the benchmark SAA method, the Kernel Method performs most prominently, achieving up to an approximately 3% reduction in general cost across all test cases, while also improving computational efficiency by up to 80%. We also provide extensive practical insights into our approach.