{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/98234"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/98234","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Stochastic optimization with decisions truncated by random variables and its applications in operations","abstract":"We study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.","abstract_html":"We study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.","abstract_has_math":false,"creators":["Gao, Xiangyu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Chen, Xin","Lim, Michael","Wang, Qiong","Xin, Linwei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-09-29T16:39:09Z","date_published":"2017-09-29T16:39:09Z","updated_at":"2026-07-22T22:24:35Z","subjects":["Stochastic optimization","Convexity","Supply capacity uncertainty","Revenue management"],"languages":["en"],"rights":["Copyright 2017 Xiangyu Gao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/98234","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Xin","Lim, Michael","Wang, Qiong","Xin, Linwei"]},{"key":"dc:creator","label":"Author","values":["Gao, Xiangyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-09-29T16:39:09Z","2019-09-30T09:15:26Z","2017-06-26","2017-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Stochastic optimization","Convexity","Supply capacity uncertainty","Revenue management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Xiangyu Gao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/98234"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-08-01","The student, Xiangyu Gao, accepted the attached license on 2017-06-23 at 09:21.","The student, Xiangyu Gao, submitted this Dissertation for approval on 2017-06-23 at 09:23.","This Dissertation was approved for publication on 2017-06-26 at 13:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11220 on 2017-09-29 at 11:13:44","Made available in DSpace on 2017-09-29T16:39:09Z (GMT). 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The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-08-01","The student, Xiangyu Gao, accepted the attached license on 2017-06-23 at 09:21.","The student, Xiangyu Gao, submitted this Dissertation for approval on 2017-06-23 at 09:23.","This Dissertation was approved for publication on 2017-06-26 at 13:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11220 on 2017-09-29 at 11:13:44","Made available in DSpace on 2017-09-29T16:39:09Z (GMT). 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