Contextual Stochastic Optimization for Omnichannel Multicourier Order Fulfillment Under Delivery Time Uncertainty

• 成果类型: Article
• 署名作者: Ye, Tinghan; Cheng, Sikai; Hijazi, Amira; Van Hentenryck, Pascal
• 署名单位: University System of Georgia; Georgia Institute of Technology
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2024.1328
• 发表日期: 2026
• 关键词: retail; management; logistics; analytics

摘要

Problem definition: The paper studies a large-scale order fulfillment problem for a leading e-commerce company in the United States. The challenge involves selecting fulfillment centers and shipping carriers with observational data only to efficiently process orders from a vast network of physical stores and warehouses. The company's current practice relies on heuristic rules that choose the cheapest fulfillment and shipping options for each unit without considering opportunities for batching items or the reliability of carriers in meeting expected delivery dates. Methodology/results: The paper develops a data-driven contextual stochastic optimization (CSO) framework that integrates distributional forecasts of delivery time deviations with stochastic and robust order fulfillment optimization models. The framework optimizes the selection of fulfillment centers and carriers, accounting for item consolidation and delivery time uncertainty. Validated on a real-world data set containing tens of thousands of products, each with hundreds to thousands of fulfillment options, the proposed CSO framework significantly enhances the accuracy of meeting customer-expected delivery dates compared with current practices. It provides a flexible balance between reducing fulfillment costs and managing delivery time deviation risks, emphasizing the importance of contextual information and distributional forecasts in order fulfillment. Managerial implications: This is the first study of an omnichannel multi-courier order fulfillment problem with delivery time uncertainty through the lens of contextual optimization, fusing machine learning and optimization. The results offer actionable guidance for retailers to enhance service quality and customer satisfaction while balancing cost efficiency and risk, supporting higher retention and profitability.