Dynamic assignment of delivery order bundles to in-store customers

作     者：Mancini, Simona Ulmer, Marlin W. Gansterer, Margaretha 

作者机构：Univ Palermo Dept Engn Palermo Italy Univ Klagenfurt Dept Operat Energy & Environm Management Klagenfurt Austria Otto von Guericke Univ Dept Management Sci Magdeburg Germany 

出 版 物：《OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE》 (Omega)

年 卷 期：2025年第133卷

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) , Germany Emmy Noether Programme FWF Austrian Science Fund, Austria [P 34502-N] 

主　　题：Crowdsourcing Order bundling Sequential decision making Approximate dynamic programming 

摘      要：Many larger grocery stores offer home delivery services. However, the delivery cost is usually high and such services are rarely profitable. One way of reducing cost is by outsourcing some orders to in-store customers fora compensation. While initially single orders were dynamically assigned to customers, companies started exploring the assignment of order bundles instead to reduce per-order compensation and exploit consolidation potential. We investigate the value of dynamic assignment of bundles in this work. To this end, we consider a setting where all orders are known and, over time, unknown in-store customers enter the system fora short time and offer transportation of bundles of orders for compensation. The store decides dynamically which bundle to assign to which in-store customer (if any). At the end of the time horizon, the remaining orders are delivered by a dedicated fleet of store employees. The goal of the store is to minimize the compensation prices together with the delivery cost. We propose a threshold-based policy with scenario-based tuning. Popularity and compensation price thresholds are determined a priori by solving a set of perfect information scenarios. In every state, bundles are only assigned if they are popular enough and the compensation is comparably low. The thresholds (i.e., popularity threshold and compensation threshold) are adapted over time to account for the decrease in assignment opportunities. We show the effectiveness of our policy in a comprehensive computational study and highlight the value of bundle assignments compared to assigning individual orders. We further show that our strategy not only reduces the compensation paid to in-store customers but also the final routing cost.