{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/45286"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/45286","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Empirical comparison of robust, data driven and stochastic optimization","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Wang, Yanbo, S.M. Massachusetts Institute of Technology"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Computation for Design and Optimization Program","school":null,"contributors":[],"advisors":["Dimitris J. Bertsimas."],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-22T22:21:16Z","subjects":["Computation for Design and Optimization Program."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/45286","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Dimitris J. Bertsimas."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Computation for Design and Optimization Program"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Computation for Design and Optimization Program."]},{"key":"dc:creator","label":"Author","values":["Wang, Yanbo, S.M. 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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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/45286"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes bibliographical references (leaf 49).","Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Empirical comparison of robust, data driven and stochastic optimization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Dimitris J. Bertsimas."],"dc:contributor.department":["Massachusetts Institute of Technology. Computation for Design and Optimization Program"],"dc:contributor.other":["Massachusetts Institute of Technology. Computation for Design and Optimization Program."],"dc:creator":["Wang, Yanbo, S.M. Massachusetts Institute of Technology"],"dc:date.accessioned":["2009-04-29T17:20:38Z"],"dc:date.available":["2009-04-29T17:20:38Z"],"dc:date.issued":["2008"],"dc:description":["Includes bibliographical references (leaf 49).","Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008."],"dc:description.abstract":["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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/45286"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Computation for Design and Optimization Program."],"dc:title":["Empirical comparison of robust, data driven and stochastic optimization"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:16Z"}