Title:Recent Advances in Sequential Decision-Making Problems Under Uncertainty
Presenter:Dr. Guanglin Xu, Postdoctoral Fellow,Institute for Mathematics and Its Applications, University of Minnesota
Time:2019.6.4 16:00-18:00
Location:A1137
Invited by: Yashuai Li
Abstract
Sequential decision making under uncertainty is an important approach to solve problems arising in many contexts including inventory control, healthcare, and revenue management among others. In this talk, we discuss two-stage robust linear optimization with uncertain right-hand sides. We reformulate the two-stage problem into a conic linear optimization problem, which in turn leads to a class of tractable, semidefinite-based approximations that are at least as strong as the linear decision rule approximation. We investigate several examples from the literature demonstrating that our tractable approximations significantly improve the linear decision rule approximation. If time permits, we will discuss an extension to the generic multi-stage robust linear optimization problems.
About the presenter:
Dr. Guanglin Xu is a postdoctoral research fellow in the Institute for Mathematics and its Applications (IMA) at the University of Minnesota, where he is involved in both academic research at IMA and industrial applications at Cargill. His research interests are in the areas of data analytics and operations research with focuses on data-driven decision making, decision making under uncertainty, and their applications in healthcare, supply chain management, and energy systems. He received his Ph.D. in Management Sciences in 2017 from the University of Iowa and an M.S. in Industrial Engineering in 2012 from the same institution.
