交货时间不确定情况下全渠道多快递员订单履行的情境随机优化

Tinghan Ye, Sikai Cheng, Amira Hijazi, Pascal Van Hentenryck
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引用次数: 0

摘要

本文研究了美国一家领先电子商务公司的大规模订单履行问题。该难题涉及仅利用观察数据选择履约中心和运输公司,以有效处理来自庞大实体店和仓库网络的订单。该公司目前的做法依赖于启发式规则,即为每个单位选择最便宜的履行和运输方案,而不考虑分批发货的机会或承运商在满足预期交货日期方面的可靠性。本文开发了一个数据驱动的上下文随机优化(CSO)框架,该框架将交货时间偏差的分布预测与随机和稳健的订单履行优化模型整合在一起。该框架优化了配送中心和承运商的选择,同时考虑了项目合并和交付时间的不确定性。拟议的 CSO 框架在包含数万种产品(每种产品都有数百种履行选项)的真实数据集上进行了验证,与当前的做法相比,该框架显著提高了满足客户预期交货日期的准确性。它在降低执行成本和管理交货时间偏差风险之间实现了灵活的平衡,强调了上下文信息和配送预测在订单执行中的重要性。这是第一篇通过上下文优化的视角研究具有交货时间不确定性的全渠道多快递员订单履行问题的论文,将机器学习和优化融为一体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contextual Stochastic Optimization for Omnichannel Multi-Courier Order Fulfillment Under Delivery Time Uncertainty
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. 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 of fulfillment options, the proposed CSO framework significantly enhances the accuracy of meeting customer-expected delivery dates compared to 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. This is the first paper that studies the omnichannel multi-courier order fulfillment problem with delivery time uncertainty through the lens of contextual optimization, fusing machine learning and optimization.
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