Asymptotic Optimality of Simple Policies for Stochastic Inventory Systems with Delivery Lead Time and Purchase Returns

信息管理与工程学院102室
上海财经大学（第三教学楼西侧）

上海市杨浦区武东路100号

Jinzhi Bu is an Assistant Professor in the Department of Logistics and Maritime Studies at the Hong Kong Polytechnic University. Her research interests include stochastic modeling and optimization, statistical and machine   learning, data-driven decision making, and their applications to supply chain and revenue management. Prior to joining PolyU, she was a postdoctoral associate at Massachusetts Institute of Technology and obtained a Ph.D. degree from the Chinese University of Hong Kong and a B.S. degree from Nanjing University.

We study a multi-period inventory control problem with a fixed replenishment lead time and stochastic purchase returns. Each unit of sales can be returned   within a fixed return window after purchase and used to fulfill new demands.   Unmet demands are either lost or backlogged. The objective is to find a   replenishment policy to minimize the long-run average cost. Due to the   presence of stochastic returns, even the optimal policy for the system with   zero lead time is already complex. We focus on two simple policies: the   base-stock policy and the myopic policy. We prove that, under a large class   of demand distributions, the base-stock policy and the myopic policy are   asymptotically optimal as the unit shortage penalty cost goes to infinity.   Our results suggest the practical use of simple policies, especially for retail applications where the target service level is known to be very high.   Our numerical results further demonstrate that, by appropriately incorporating the return probabilities and accounting for the returns   dynamics, the empirical performances of both policies can be substantially   improved. This is a joint work with Huanan Zhang and Stefanus Jasin.