Submitted to Operations Research manuscript (Please, provide the manuscript number!) Data-Driven Robust Optimization

The last decade witnessed an explosion in the availability of data for operations research applications. Moti-vated 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 flexible and widely applicable, and robust optimization problems built from our new sets are computationally tractable, both theoretically and practically. Furthermore, optimal solutions to these problems enjoy a strong, finite-sample probabilistic guar-antee. We describe concrete procedures for choosing an appropriate set for a given application and applying our approach to multiple uncertain constraints. Computational evidence in portfolio management and queu-ing confirm that our data-driven sets significantly outperform traditional robust optimization techniques whenever data is available. Key words: robust optimization, data-driven optimization History: This paper was first submitted in August 2013. 1