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Recent Advances in Sequential Decision-Making Problems Under Uncertainty

发布时间: 2019/05/29 23:33:34     点击次数:次   打印本页

讲座题目:Recent Advances in Sequential Decision-Making Problems Under Uncertainty

主讲人:Dr. Guanglin Xu, Postdoctoral Fellow,Institute for Mathematics and Its Applications, University of Minnesota

时间:2019年6月4日(星期二),16:00-18:00

教室:北京航空航天大学新主楼A座1137室

邀请人:李亚帅

讲座内容:

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.

主讲人介绍:

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.