Data-driven robust optimization

• Published in: Mathematical programming Vol. 167; no. 2; pp. 235 - 292
• Main Authors: Bertsimas, Dimitris, Gupta, Vishal, Kallus, Nathan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018; Springer Nature B.V
• Subjects: Calculus of Variations and Optimal Control; Optimization; Combinatorics; Design optimization; Full Length Paper; Mathematical and Computational Physics; Mathematical Methods in Physics; Mathematics; Mathematics and Statistics; Mathematics of Computing; Nonlinear programming; Numerical Analysis; Operations research; Optimization techniques; Portfolio management; Queues; Statistical analysis; Theoretical; Uncertainty; Hypothesis testing; Robust optimization; Chance-constraints; 62H15 (Multivariate Analysis: Hypothesis Testing); 80M50 (Optimization: Operations research, mathematical programming); Data-driven optimization

The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests. The approach is flexibl...