Empirical comparison of robust, data driven and stochastic optimization
Includes bibliographical references (leaf 49).
| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/45286 |
| _version_ | 1811077044930347008 |
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| author | Wang, Yanbo, S.M. Massachusetts Institute of Technology |
| author2 | Dimitris J. Bertsimas. |
| author_facet | Dimitris J. Bertsimas. Wang, Yanbo, S.M. Massachusetts Institute of Technology |
| author_sort | Wang, Yanbo, S.M. Massachusetts Institute of Technology |
| collection | MIT |
| description | Includes bibliographical references (leaf 49). |
| first_indexed | 2024-09-23T10:37:09Z |
| format | Thesis |
| id | mit-1721.1/45286 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:37:09Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/452862022-01-13T07:54:53Z Empirical comparison of robust, data driven and stochastic optimization Wang, Yanbo, S.M. Massachusetts Institute of Technology Dimitris J. Bertsimas. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Massachusetts Institute of Technology. Computation for Design and Optimization Program Computation for Design and Optimization Program. Includes bibliographical references (leaf 49). Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. In this thesis, we compare computationally four methods for solving optimization problems under uncertainty: * Robust Optimization (RO) * Adaptive Robust Optimization (ARO) * Data Driven Optimization (DDO) * stochastic Programming (SP) We have implemented several computation experiments to demonstrate the different performance of these methods. We conclude that ARO outperform RO, which has a comparable performance with DDO. SP has a comparable performance with RO when the assumed distribution is the same as the true underlying distribution, but under performs RO when the assumed distribution is different from the true distribution. by Wang, Yanbo. S.M. 2009-04-29T17:20:38Z 2009-04-29T17:20:38Z 2008 2008 Thesis http://hdl.handle.net/1721.1/45286 311861975 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 49 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Computation for Design and Optimization Program. Wang, Yanbo, S.M. Massachusetts Institute of Technology Empirical comparison of robust, data driven and stochastic optimization |
| title | Empirical comparison of robust, data driven and stochastic optimization |
| title_full | Empirical comparison of robust, data driven and stochastic optimization |
| title_fullStr | Empirical comparison of robust, data driven and stochastic optimization |
| title_full_unstemmed | Empirical comparison of robust, data driven and stochastic optimization |
| title_short | Empirical comparison of robust, data driven and stochastic optimization |
| title_sort | empirical comparison of robust data driven and stochastic optimization |
| topic | Computation for Design and Optimization Program. |
| url | http://hdl.handle.net/1721.1/45286 |
| work_keys_str_mv | AT wangyanbosmmassachusettsinstituteoftechnology empiricalcomparisonofrobustdatadrivenandstochasticoptimization |