【学术讲座】高效随机模拟优化算法及其应用

Efficient Stochastic Simulation Optimization Algorithms and Their Applications

Date & Time: 9:30-11:00, Friday 19^th, July 2019

Venue: Room 2101, Tongji Building A

Language: English

Speaker: Prof. Jie Xu, (George Mason University)

ABSTRACT

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.

Speaker’s Bio

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.

All the faculties and students are welcome to attend this lecture!