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. 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 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 guarantee. We propose concrete guidelines for practitioners and illustrate our approach with applications in portfolio management, queueing, and facility location. Computational evidence confirms 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