Optimization with multivariate stochastic preference constraints
Seminar by Nilay Noyan Mechanical Engineering Sabancı University
For many decision making problems under uncertainty, it is crucial to specify decision makers' risk preferences based on multiple stochastic performance measures. We consider a class of multicriteria stochastic optimization problems that feature risk- averse benchmarking constraints based on conditional value-at-risk and second- order stochastic dominance. We develop alternative mixed-integer programming formulations and solution methods for cut generation problems arising in optimization under such multivariate risk constraints. We give the complete linear description of two non-convex substructures appearing in these cut generation problems. We present computational results that show the effectiveness of our proposed models and methods. (Joint work with Simge Küçükyavuz, The Ohio State University) Nilay Noyan is an Associate Professor in the Manufacturing Systems and Industrial Engineering Program at Sabancı University, Turkey. She is a recipient of the Young Scientist (BAGEP) Research Award of the Science Academy, Turkey. She received her Ph.D. degree in operations research from Rutgers University, USA, in 2006. Her research interests include optimization, stochastic programming, risk modeling, and stochastic optimization applications with an emphasis on sustainable urban transportation, airline revenue management, and disaster relief network design.