题目：Efficient Stochastic Simulation Optimization Algorithms and Their Applications
Dr. Jie Xu is an Associate Professor of Systems Engineering & Operations Research
at George Mason University, Fairfax, VA, USA. He received his Ph.D. degree in Industrial Engineering and Management Sciences from Northwestern University, Evanston, IL, USA, in 2009. His research interests are the modeling, simulation, and optimization of complex stochastic systems. His expertise includes stochastic simulation optimization, data analytics, and their applications in cloud computing, energy systems, financial systems, health care, manufacturing, and transportation. His work has been sponsored by the National Science Foundation, Air Force Office of Scientific Research, Jeffress Trust of Interdisciplinary Research, and Oak Ridge Associated Universities.
Stochastic simulation optimization provides a powerful and general decision-support tool in a wide range of industries including cloud computing, energy, financial services, health care, manufacturing, and transportation. However, major difficulties arise when the simulation is time-consuming and computational budget is limited. In this talk, we discuss several efforts that aim to improve the computational efficiency of stochastic simulation optimization algorithms for large-scale applications. First, we discuss a class of locally convergent adaptive stochastic search algorithms. We then illustrate the benefit of using multi-fidelity models in simulation optimization. Finally, we present a power grids hardening problem and a semiconductor production planning problem to demonstrate the potential of stochastic simulation optimization.