Speaker: Prof.Jian Yang, University of Texas at Austin

Time: 10:00-11:00 August 30th, 2019

Venue: Tongji Building Block A  Room206

Abstract:

We study a dynamic inventory control problem involving fixed setup costs and random demand distributions. With an infinite planning horizon, model primitives including costs and distributions are set to be stationary. Under a given demand distribution, an (s,S) policy has been known to minimize the long-run per-period average cost. However, we depart from the traditional literature by allowing the stationary demand distribution to be largely unknown. Our goal is to rein in the long-run growth of the regret resulting from applying a policy that strives to learn the underlying demand while simultaneously meting out ordering decisions based on its learning. We propose a policy that controls the pace at which a traditional (s,S)-computing algorithm is applied to the empirical distribution of the demand learned over time. The regret incurred from the policy is shown to grow over time at an -sized rate.

Speaker’s BIO:

Jian Yang obtained his Ph.D. in Management Science from the University of Texas at Austin.  After working for the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology, he is now a professor at the Department of Management Science and Information Systems, Rutgers Business School, Rutgers University.  Dr. Yang’s research interests are in combinatorial optimization, logistics, production and inventory control, dynamic pricing, and game theory.  Recently, he is particularly interested in the role played by risk and ambiguity in dynamic inventory-price control and game-theoretical settings.