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Data Driven Analysis and Retrospective Optimization

Honggang Wang
ISE Assistant Professor
Rutgers University
Friday, April 21, 2017  2:30-3:30pm
JDT 410


In this talk, I will first present R-SPLINE (Retrospective Optimization using Simplex Linear Interpolation and Neighborhood Evaluation) for decision making problems associated with complex stochastics systems with integer decision variables. R-SPLINE is an optimization framework with sequential sample-average (data-driven) approximations and guarantees almost-sure convergence under certain conditions. R-SPLINE has been extended for other simulation optimization problems with mixed integer variables. I will also talk about new multi-objective optimization ideas using ZIGZAG search for multi-criteria decision making under systems uncertainty.

I will present numerical studies applying R-SPLINE and zigzag to application problems in oil/gas field development, carbon sequestration, smart grid, and power systems. Numerical results based on the studied cases demonstrate the efficiency of the discussed R-SPLINE and zigzag algorithms.

Bio: 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 Co. in 2016 for his work in oil/gas projects. He has won 2016 best paper award at Geothermal Research Council annual meeting, Sacramento CA.