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Research | Deng Qiyuan: Optimal Policies and Heuristics to Match Supply with Demand for Online Retailing

Release time:19 December 2024

Over the past decade, global e-commerce retail sales have experienced explosive growth, establishing online retail as a crucial component of the global retail landscape. However, this sector also faces increasingly complex challenges in supply and demand matching.

Recently, a collaborative study by Professor Deng Qiyuan from the School of Management and Economics at The Chinese University of Hong Kong, Shenzhen, titled Optimal Policies and Heuristics To Match Supply With Demand For Online Retailing, addresses the issue of supply and demand alignment in online retail. The research aims to explore effective strategies for optimizing the equilibrium between supply and demand, thereby enhancing operational efficiency and economic performance for businesses. This study has been accepted and published in the top journal Manufacturing & Service Operations Management.

Author

Deng Qiyuan

Assistant Professor, School of Management and Economics, CUHK-Shenzhen

Research Area

Online Platforms, E-commerce, and Artificial Intelligence

Co-authors

Xiaobo Li

National University of Singapore

Yun Fong Lim

Singapore Management University

Fang Liu

Durham University

 

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 multiperiod 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 nongreedy algorithm to find the optimal storage policy, which preserves a nested property: Among all nonempty warehouses, a smaller-index warehouse contains all the products stored in a larger-index warehouse. We also analytically characterize the optimal ordering policy. The multizone case is unfortunately intractable analytically, and we propose an efficient heuristic to solve it, which involves a nontrivial 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 heuristic improves the average profit by up to 16% compared with a dedicated policy adopted by the retailer. The hybrid heuristic continues to outperform the benchmarks for larger networks with various structures.