Distributionally Robust Optimization under Decision-Dependent Ambiguity Set <\/p>\n
Assoc. Prof. Dr. Nilay Noyan
\nIndustrial Engineering, Sabanc\u0131 University <\/p>\n
May 17, 1:40 p.m.
\nPlace: EA-409 <\/p>\n
We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasserstein metrics. We discuss the main computational challenges in solving the problems of interest, and provide an overview of various settings leading to tractable formulations. Some of the arising side results, such as the mathematical programming expressions for robustified risk measures in a discrete space, are also of independent interest. Finally, we rely on state-of-the-art modeling techniques from humanitarian logistics and machine scheduling to arrive at potentially practical applications. <\/p>\n
Nilay Noyan is an Associate Professor in the Industrial Engineering Program at Sabanc\u0131 University, Turkey. She received her B.S. degree in Industrial Engineering from Middle East Technical University, Turkey, in 2001, and her Ph.D. degree in Operations Research from Rutgers University (RUTCOR), USA, in 2006. Her research interests include decision making under uncertainty, stochastic programming, risk modeling, large-scale optimization, and stochastic optimization applications with an emphasis on humanitarian logistics. <\/p>\n","protected":false},"excerpt":{"rendered":"
Distributionally Robust Optimization under Decision-Dependent Ambiguity Set Assoc. Prof. Dr. Nilay Noyan Industrial Engineering, Sabanc\u0131 University May 17, 1:40 p.m. Place: EA-409 We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability [\u2026] <\/p>\n