Event Details

Dr Belen Martin-Barragan from the University of Edinburgh will present a seminar to the Management Science Department

Abstract: In this research, we develop and explore a modified version of the smart predict-then-optimize (SPO) strategy, which considers uncertainties in data prediction and inputs when optimizing. Building on the fundamental principles of the SPO model, our method focuses on refining predictions to reduce regret when those predictions shape the parameters of an optimization problem. We shift from a fixed, deterministic approach to one where data inaccuracies introduce uncertainty, and we apply robust optimization methods to address these uncertainties. Specifically, we study three types of robustness (worst-case robustness, strict robustness, and intermediate robustness) that tolerate varying levels of suboptimality and thus replicate different robustness-enforcing strategies. We assess our robust optimization models considering both uncertainties in the predictions and in the covariates. Our numerical results show significant out-of-sample performance improvements under randomly generated covariate disturbances, compared to the classic SPO approach, even when a small sample size is used.

Keywords

• Optimization under uncertainty
• Multi-level optimization
• Optimization for learning and data analysis