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

61 Pages Posted: 2 Nov 2021 Last revised: 2 Sep 2022

See all articles by Recep Yusuf Bekci

Recep Yusuf Bekci

McGill University - Desautels Faculty of Management

Mehmet Gumus

McGill University - Desautels Faculty of Management

Sentao Miao

University of Colorado Boulder

Date Written: October 29, 2021

Abstract

Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study a two-echelon inventory control problem called the One-Warehouse Multi-Store (OWMS) problem when the demand distribution is unknown. This system has a central warehouse that receives an initial replenishment and distributes its inventory to multiple stores in each time period during a finite horizon. The goal is to minimize the total expected cost which consists of shipment costs, holding costs, lost-sales costs, and end-of-horizon disposal costs. The OWMS system is ubiquitous in supply chain management, yet its optimal policy is notoriously difficult to calculate even under complete demand distribution case. In this work, we consider the OWMS problem when the demand distribution is unknown a priori. By observing the censored demand, the firm has to jointly learn the demand and make inventory control decisions on the fly. We first develop a learning algorithm based on empirical demand distribution and prove an upper bound on its theoretical performance when the demand information is uncensored. Then, in the censored demand case, we propose a more sophisticated algorithm based on a primal-dual learning and optimization approach. Results show that both algorithms have great theoretical and empirical performances.

Keywords: inventory control, one-warehouse multi-store system, demand learning, censored demand, online learning

Suggested Citation

Bekci, Recep Yusuf and Gumus, Mehmet and Miao, Sentao, Inventory Control and Learning for One-Warehouse Multi-Store System with Censored Demand (October 29, 2021). Available at SSRN: https://ssrn.com/abstract=3952925 or http://dx.doi.org/10.2139/ssrn.3952925

Recep Yusuf Bekci (Contact Author)

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Mehmet Gumus

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Sentao Miao

University of Colorado Boulder ( email )

256 UCB
Boulder, CO CO 80300-0256
United States

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