Optimal Policies and Heuristics To Match Supply With Demand For Online Retailing

Denga, Qiyuan; Lic, Xiaobo; Fong Limd, Yun; Liu, Fang

Abstract

Problem definition: We consider an online retailer selling multiple products to different zones over a finite horizon with multiple periods. At the start of the horizon, the retailer orders the products from a single supplier and stores them at multiple warehouses. The retailer determines the products' order quantities and their storage quantities at each warehouse subject to its capacity constraint. At the end of each period, after random demands in the period are realized, the retailer chooses the retrieval quantities from each warehouse to fulfill the demands of each zone. The objective is to maximize the retailer's expected profit over the finite horizon. Methodology/results: For the single-zone case, we show that the multi-period problem is equivalent to a single-period problem and the optimal retrieval decisions follow a greedy policy that retrieves products from the lowest-cost warehouse. We design a non-greedy algorithm to find the optimal storage policy, which preserves a nested property: Among all non-empty warehouses, a smaller-index warehouse contains all the products stored in a larger-index warehouse. We also analytically characterize the optimal ordering policy. The multi-zone case is unfortunately intractable analytically and we propose an efficient heuristic to solve it, which involves a non-trivial hybrid of three approximations. This hybrid heuristic outperforms two conventional benchmarks by up to 22.5% and 3.5% in our numerical experiments with various horizon lengths, fulfillment frequencies, warehouse capacities, demand variations, and demand correlations. Managerial implications: A case study based on data from a major fashion online retailer in Asia confirms the superiority of the hybrid heuristic. With delicate optimization, the heuris-tic improves the average profit by up to 16% compared to a dedicated policy adopted by the retailer. The hybrid heuristic continues to outperform the benchmarks for larger networks with various structures.

Citation

Denga, Q., Lic, X., Fong Limd, Y., & Liu, F. (online). Optimal Policies and Heuristics To Match Supply With Demand For Online Retailing. Manufacturing & Service Operations Management, https://doi.org/10.1287/msom.2021.0394

• Journal Article Type: Article
• Acceptance Date: Jun 28, 2024
• Online Publication Date: Jul 26, 2024
• Deposit Date: Jul 1, 2024
• Publicly Available Date: Jul 26, 2024
• Journal: Manufacturing & Service Operations Management
• Print ISSN: 1523-4614
• Electronic ISSN: 1526-5498
• Publisher: Institute for Operations Research and Management Sciences
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1287/msom.2021.0394
• Keywords: online seasonal sales; product ordering; inventory allocation; order fulfillment; multi- ple periods
• Public URL: https://durham-repository.worktribe.com/output/2513291