Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6895496 | European Journal of Operational Research | 2016 | 21 Pages |
Abstract
We consider robust stochastic optimization problems for risk-averse decision makers, where there is ambiguity about both the decision maker's risk preferences and the underlying probability distribution. We propose and analyze a robust optimization problem that accounts for both types of ambiguity. First, we derive a duality theory for this problem class and identify random utility functions as the Lagrange multipliers. Second, we turn to the computational aspects of this problem. We show how to evaluate our robust optimization problem exactly in some special cases, and then we consider some tractable relaxations for the general case. Finally, we apply our model to both the newsvendor and portfolio optimization problems and discuss its implications.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
William B. Haskell, Lunce Fu, Maged Dessouky,