(Virtual) Seminar Series | Dynamic Risk-Aware Optimization with Decision-Dependent Uncertainty

All dates for this event occur in the past.

Seminar by Xian Yu, Ph.D. candidate

Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor

 

Xian Yu

Sequential optimization is of vital importance to the design and operations of real-world complex systems in transportation, manufacturing, energy, etc. Oftentimes, input parameters of these systems such as supply and demand, may depend on previous resource-allocation decisions, and under such decision-dependent uncertainty, decision makers seek optimal dynamic solutions with robustness guarantees when the exact distribution of the uncertain parameters is not fully known. In this talk, we introduce models and algorithms for a dynamic distributionally robust optimization problem under endogenous uncertainty. We derive either mixed-integer linear programming (MILP) or mixed-integer semidefinite programming (MISDP) reformulations for the min-max Bellman equations, and for the latter we show how to obtain upper and lower bounds of the optimal objective value. We employ the Stochastic Dual Dynamic integer Programming (SDDiP) method for solving multistage MILP models. Our numerical results based on facility-location instances show the computational efficacy of our approaches and demonstrate the cost effectiveness of considering decision-dependent uncertainty in the dynamic risk-aware optimization framework. We also discuss future research including ways of generalizing this framework and its wide applications in shared mobility, supply chain management, and resource allocation in healthcare.

Xian Yu received her B.S. degree in Mathematics and Applied Mathematics from Xi’an Jiaotong University, China, in 2017, and is currently a Ph.D. candidate in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor. Her research interest lies in sequential decision-making and optimization under uncertainty, with applications in transportation, logistics, supply chain management, and healthcare-related operations management. Her dissertation aims to bridge theories in stochastic integer programming, distributionally robust optimization, and dynamic programming, as well as to develop efficient algorithms for solving large-scale complex optimization problems. Xian was an Investment Research Intern at Acadian Asset Management in summer 2020 and a Research Scientist Intern at Amazon’s Supply Chain Optimization Technologies (SCOT) team in summer 2021, where she worked on various finance and transportation problems using stochastic dynamic optimization. She has collaborated with Ford Motor Company on mobility-related projects through the Ford-UM research alliance program. Xian is the recipient of the IOE Murty Prize for the Best Student Research Paper on Optimization in 2021 and is named a Michigan Institute for Computational Discovery and Engineering Student Fellow in 2019.

Category: Seminars