Contextual Stochastic Optimization for Omnichannel Multicourier Order Fulfillment Under Delivery Time Uncertainty
研究美国电商巨头在配送时间不确定下，如何利用数据驱动的情境随机优化框架选择履约中心和承运商，提升准时交付率并平衡成本与风险，为零售商提供可操作的管理建议。
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 multicourier 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. History: This paper has been accepted as part of the 2025 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This research was partly supported by the NSF AI Institute for Advances in Optimization [Award 2112533]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1328 .