674 / 2024-05-07 10:31:05

Robust Concave Utility Maximization over Chance Constraints

Robust optimization,Chance constraints,robust expected utility

This talk presents an expected utility problem with chance constraints and incomplete information on a decision maker's utility function. The model maximizes the worst-case expected utility of random outcome over a set of concave functions within a novel ambiguity set, while the underlying probability distribution is known. To obtain computationally tractable formulations, we employ a discretization approach to derive a max-min chance-constrained approximation that is further reformulated as a mixed-integer program. We show that the discrete approximation converges to the true counterpart under mild assumptions. We also present a row generation algorithm for optimizing the max-min program. A computational study for a bin-packing problem and a multi-item newsvendor problem is conducted to demonstrate the benefit of the proposed framework and the computational efficiency of our algorithm.





(备注：Track 15, session name: Robust and Stochastic Decision-Making under U ncertainty, organized by Chun Peng, Beijing Jiaotong University. Thanks.)