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 the heart of a digital twin is often a stochastic simulation model. However, the critical distinction from traditional simulation lies in the synchronization between the real system and its digital twin via streaming data in real time. Therefore, the growing prevalence of digital twins brings new challenges to simulation analysis and optimization, calling for data-driven techniques that traditionally do not have a significant presence in simulation literature. In this talk, I will present a few of our recent works that tackle simulation/stochastic optimization problems where the underlying distribution is unknown and estimated with input data, especially streaming data arriving sequentially over time.

Enlu Zhou is a Professor in the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Institute of Technology. She received the B.S. degree with highest honors in electrical engineering from 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 Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign from 2009-2013. She is a recipient of the AFOSR Young Investigator award, NSF CAREER award, the INFORMS Simulation Society Outstanding Publication award, and the Best Theoretical Paper award at the Winter Simulation Conference (twice). She has served as an associate editor for Journal of Simulation, IEEE Transactions on Automatic Control, and Operations Research. Currently, she is the Vice President and President-Elect of the INFORMS Simulation Society.