633 / 2024-04-28 21:49:42

An On-Target End-to-End Data-Driven Approach to Solving the Order-Holding Problem Faced By Online Retailing Platforms

Online retailing,Order holding,Personalized threshold strategy,Joint prediction and optimization

This paper addresses the challenge of determining the optimal duration for an online retail platform to hold orders from individual customers before dispatching them to third-party logistics companies. Specifically, we propose an end-to-end data-driven approach that directly learns personalized order-holding policies from available data, as opposed to employing the predict-then-optimize framework outlined in Chen et al. (2023). The data-driven approach is subsequently reformulated as a mixed-integer linear programming (MILP) problem aimed at minimizing in-sample holding times while achieving the desired consolidation rate. Leveraging real data provided by the 2020 MSOM data-driven challenge, we conduct numerical experiments to illustrate that our proposed end-to-end data-driven method can substantially reduce holding times compared to the predict-then-optimize framework proposed by Chen et al. (2023). Furthermore, we identify a critical moderating parameter that influences the comparative performance of the two methods, aiding in the selection process between them.