报告内容摘要：

Many real-life systems require simulation techniques to evaluate the system performance and facilitate decision making. Stochastic simulation is driven by input model — a collection of probability distributions that model the system stochasticity. The choice of the input model is crucial for successful modeling and analysis via simulation. When there are past observed data of the system stochasticity, we can utilize these data to construct an input model. However, there is only a finite amount of data in practice, so the input model based on data is always subject to uncertainty, which is the so-called input (model) uncertainty. Therefore, a typical stochastic simulation faces two types of uncertainties: one is the input (model) uncertainty, and the other is the intrinsic stochastic uncertainty. In this talk, I will discuss our recent work on how to assess the risk brought by the two types of uncertainties and how to make optimal decisions under these uncertainties.
 
报告人简介：

Enlu Zhou 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. From 2009-2013 she was an assistant professor in the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign. Since 2013 she has been in the H. Milton School of Industrial & Systems Engineering at Georgia Institute of Technology. Her research interests include Markov decision processes, simulation optimization, and Monte Carlo statistical methods, with applications in financial engineering and revenue management. She is a recipient of the “Best Theoretical Paper” award at the Winter Simulation Conference in 2009, AFOSR Young Investigator award in 2012, and NSF CAREER award in 2015.
 
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