Decomposition Algorithms for Two-Stage Chance-Constrained Programs
Seminar by
Simge Küçükyavuz
Ohio State University
We study a class of chance-constrained two-stage stochastic optimization problems where second-stage feasible recourse decisions incur additional cost. In addition, we propose a new model, where ``recovery" decisions are made for the infeasible scenarios to obtain feasible solutions to a relaxed second-stage problem. We develop decomposition algorithms with specialized optimality and feasibility cuts to solve this class of problems. Computational results on a chance-constrained resource planing problem indicate that our algorithms are highly effective in solving these problems compared to a mixed-integer programming reformulation and a naive decomposition method.
This is joint work with Xiao Liu and Jim Luedtke
Bio: Simge Küçükyavuz is an Associate Professor in the Integrated Systems Engineering Department at the Ohio State University. She received her MSc and PhD degrees from the University of California, Berkeley, and her BS degree from the Middle East Technical University. Her interests are in mixed-integer programming, large-scale optimization, optimization under uncertainty, and their applications. Her research is supported by multiple grants from the National Science Foundation, including the 2011 CAREER Award. She serves on the editorial boards of several journals, including Computational Optimization and Applications, Networks, and Journal of Global Optimization.
