【Topic】
Inventory Control and Learning for Fast-Fashion Industry with Censored Demand
【Host】
Prof. ZHUANG Weifen, Department of Management Science, School of Management, Xiamen University
【Abstract】
Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study a (in-season) inventory management problem for fast fashion industry 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. 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 a worst-case 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.
【Speaker】
Mehmet Gumus
（Professor, Desautels Faculty of Management, McGill University）
Mehmet Gumus is Professor of Operations Management at the Desautels Faculty of Management at McGill University. He joined McGill in 2007 from the University of California at Berkeley where he completed his Ph.D. in Industrial Engineering and Operations Research and M.A. in Economics. In his research, he focuses on supply chain management, dynamic pricing, and risk management. His papers are accepted for publication in Management Science, Operations Research, Manufacturing and Service Operations Management, Marketing Science and Production and Operations Management. He serves as AE for Production and Operations Management, and IIE Transactions. In 2017, he developed a new specialized Masters Program in Management Analytics (MMA) and since its inauguration, he is managing the MMA program as the Academic Director. In 2015, he co-founded Plannica Inc. to develop an integrated Decision Support System for the supply chain planning solution and implemented Plannica® to various industries.
