Title: Data-driven Simulation Optimization in the Age of Digital Twins
Abstract: A digital twin is a virtual representation of the real system, designed to facilitate performance analysis and decision making of the actual system. At its core, a digital twin often incorporates a simulation model. However, the critical distinction from traditional simulation lies in the synchronization between the real system and its digital twin through streaming data and the frequent need for online decision making. Therefore, the increasing prevalence of digital twins poses new challenges to simulation analysis and optimization, calling for data-driven techniques that traditionally lack a significant presence in simulation literature. In this keynote, I will discuss these emerging challenges associated with simulation optimization and present some of our recent works that address these challenges, particularly in the setting where the system randomness is modeled by distributions that are estimated from streaming data.
Bio: Enlu Zhou is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. She received the B.S. degree with highest honors in electrical engineering from Chu Kochen Honors College, Zhejiang University, China, in 2004, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park, in 2009. Prior to joining Georgia Tech in 2013, she was an assistant professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois Urbana-Champaign from 2009 to 2013. She is a recipient of the AFOSR Young Investigator award in 2012, NSF CAREER award in 2014, the INFORMS Outstanding Simulation Publication award in 2020, and the Best Theoretical Paper award at the Winter Simulation Conference twice in 2009 and 2022. She currently serves as the President of the INFORMS Simulation Society. Her research interests lie in theory, methods, and applications of simulation, stochastic optimization, and stochastic control, with applications in machine learning, robotics, systems biology, and financial engineering.