training data. More recently, data-driven robust optimization based learning models provide an intuitive robustness perspective …

We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision …

mean and variance, of the uncertain coefficients. In this paper, we propose a general robust optimization framework for the … the violation probability of the chance constraint. Based on these upper bounds, we then derive two new robust … optimization models to approximate the CCBP. Both models can be decomposed into binary programs, and one of them can be decomposed …

predictions into the optimization? We propose a scenario-based robust optimization approach that combines stochastic programming … (by constructing probabilistic scenarios), robust optimization (by protecting against adversarial perturbations within … class of problems. Using public datasets, results suggest that (i) our scenario-based robust optimization approach …