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 flexibl...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Berlin Heidelberg
2018
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| Online Access: | http://hdl.handle.net/1721.1/116834 https://orcid.org/0000-0002-1985-1003 |
| _version_ | 1826210854597558272 |
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| author | Gupta, Vishal Kallus, Nathan Bertsimas, Dimitris J |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Gupta, Vishal Kallus, Nathan Bertsimas, Dimitris J |
| author_sort | Gupta, Vishal |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T14:56:29Z |
| format | Article |
| id | mit-1721.1/116834 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:56:29Z |
| publishDate | 2018 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1168342022-09-29T11:34:21Z Data-driven robust optimization Gupta, Vishal Kallus, Nathan Bertsimas, Dimitris J Sloan School of Management Bertsimas, Dimitris J 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 National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374) 2018-07-06T17:51:59Z 2018-07-06T17:51:59Z 2017-02 2015-09 2018-01-30T04:46:13Z Article http://purl.org/eprint/type/JournalArticle 0025-5610 1436-4646 http://hdl.handle.net/1721.1/116834 Bertsimas, Dimitris, et al. “Data-Driven Robust Optimization.” Mathematical Programming, vol. 167, no. 2, Feb. 2018, pp. 235–92. https://orcid.org/0000-0002-1985-1003 en http://dx.doi.org/10.1007/s10107-017-1125-8 Mathematical Programming Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society text/xml application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Gupta, Vishal Kallus, Nathan Bertsimas, Dimitris J Data-driven robust optimization |
| title | Data-driven robust optimization |
| title_full | Data-driven robust optimization |
| title_fullStr | Data-driven robust optimization |
| title_full_unstemmed | Data-driven robust optimization |
| title_short | Data-driven robust optimization |
| title_sort | data driven robust optimization |
| url | http://hdl.handle.net/1721.1/116834 https://orcid.org/0000-0002-1985-1003 |
| work_keys_str_mv | AT guptavishal datadrivenrobustoptimization AT kallusnathan datadrivenrobustoptimization AT bertsimasdimitrisj datadrivenrobustoptimization |