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 : Contextual optimisation; Optimization under uncertainty; Robust optimisation

Bio: Belen Martín-Barragán is Reader in Management Science at The University of Edinburgh. Her research lies at the interface between Data Science and Mathematical Programming, with a special interest on explainable artificial intelligence and, more recently, integrated prediction and optimization. Her work has appeared in a variety of top-ranked journals such as European Journal of Operational Research, Risk Analysis, INFORMS Journal on Computing, Discrete Applied Mathematics, Computers and Operations Research. She is also contributed to other areas such as statistics (Journal of Applied Statistics) and economics (Economic Modelling). She was been PI of the EPSRC-funded project “Optimisation Models for Interpretable Analytics” and has collaborated in research projects with companies and organizations in sectors as diverse as construction (COSTAIN) resource management (Retain) and finance (Monetary Authority of Singapore), to cite a few.