Data-driven robust optimization

Full Length Paper


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 whenever the constraints and objective function are concave in the uncertainty. 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 queueing confirm that our data-driven sets significantly outperform traditional robust optimization techniques whenever data are available.


Robust optimization Data-driven optimization Chance-constraints Hypothesis testing 

Mathematics Subject Classification

80M50 (Optimization: Operations research, mathematical programming) 62H15 (Multivariate Analysis: Hypothesis Testing) 

Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2017

Authors and Affiliations

  1. 1.Sloan School of ManagementMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Marshall School of BusinessUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.School of Operations Research and Information EngineeringCornell University and Cornell TechNew YorkUSA

Personalised recommendations