Data-driven robust optimization
Author(s)
Gupta, Vishal
Kallus, Nathan
Bertsimas, Dimitris J
Date Issued
February 2017
Journal
Mathematical Programming
Publisher
Springer Berlin Heidelberg
Citation
Bertsimas, Dimitris, et al. “Data-Driven Robust Optimization.” Mathematical Programming, vol. 167, no. 2, Feb. 2018, pp. 235–92.
Version
Author's final manuscript
Abstract
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. Keywords: Robust optimization, Data-driven optimization, Chance-constraints, Hypothesis testing
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10107-017-1125-8
