Multi-Objective Risk-Averse Two-Stage Stochastic Programming Problems
Department of Industrial Engineering, Bilkent University
We consider a multi-objective risk-averse two-stage stochastic programming problem with a multivariate convex risk measure. We suggest a convex vector optimization formulation with set-valued constraints and propose an extended version of Benson's algorithm to solve this problem. Using Lagrangian duality, we develop scenario-wise decomposition methods to solve the two scalarization problems appearing in Benson's algorithm. Then, we propose a procedure to recover the primal solutions of these scalarization problems from the solutions of their Lagrangian dual problems. Finally, we test our algorithms on a multi-asset portfolio optimization problem under transaction costs.
This is a joint work with Çağın Ararat and Ali İrfan Mahmutoğulları.
Özlem Çavuş is currently an Assistant Professor of Industrial Engineering at Bilkent University. She received her B.S. and M.S. degrees in Industrial Engineering from Boğaziçi University in 2004 and 2007, respectively, and the Ph.D. degree in Operations Research from Rutgers Center for Operations Research (RUTCOR) at Rutgers University in 2012. Her research interests include stochastic optimization, risk-averse optimization and Markov decision processes.
Friday, December 21, 2018 at 4.00 pm in IE03