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Retrospective Optimization for Stochastic Systems

发布时间:2017-07-03 编辑:admin 来源:华科自动化学院

 

报告题目Retrospective Optimization for Stochastic Systems

人:王洪刚 助理教授Rutgers University

报告时间2017年7月7日1000

报告地点南一楼中311

摘要In this talk, I will share my research work including methodology and application in simulation based optimization. I will first present R-SPLINE (Retrospective Optimization using Simplex Linear Interpolation and Neighborhood Evaluation) for integer simulation optimization problems. R-SPLINE is a stochastic optimization framework with sequential data-driven approximations and guarantees almost-sure convergence under certain conditions. I will also talk about new multi-objective optimization ideas using zigzag search for multi-criteria decision making under systems uncertainty. Numerical results based on a number of energy production and micro-grid power cases are shown to demonstrate the efficiency of the discussed algorithms.

个人简介: Honggang Wang is assistant professor in Industrial and Systems Engineering at Rutgers University. He received his Bachelor of Science degree in Power Engineering from Shanghai Jiao Tong University, Shanghai, China, in 1996, Master of Science in Manufacturing Engineering from University of Missouri-Rolla, in 2004, and Ph.D. in Operations Research from Purdue University, West Lafayette IN, in 2009. He had worked as a Postdoctoral Scholar in Energy Resources Engineering at Stanford University for two years before he joined Rutgers, NJ in 2011. Dr. Wang has won IBM faculty award 2012 and faculty award from Tracy Energy LLC for his work in petroleum and energy projects. He has won 2016 best paper award at Geothermal Research Council annual meeting, Sacramento CA, USA.