Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand

Recep Yusuf Bekci; Mehmet Gümüş; Sentao Miao

doi:10.1287/opre.2021.0694

Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand

Recep Yusuf Bekci¹; Mehmet Gümüş²; Sentao Miao³

¹ Department of Management Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada · ² Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 0G4, Canada · ³ Leeds School of Business, University of Colorado Boulder, Boulder, Colorado 80309

Operations Research 2023

Efficient Learning Algorithms for Dynamic Inventory Allocation in Multiwarehouse Multistore Systems with Censored Demand Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry.

DOI: 10.1287/opre.2021.0694
Volume: 71 (6)
Pages: 2092-2110
Language: en
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Sources: crossref