Deviation measures in stochastic programming with mixed-integer recourse

Stochastic programming offers a way to treat uncertainty in decision problems. In particular, it allows the minimization of risk. We consider mean-risk models involving deviation measures, as for instance the standard deviation and the semideviation, and discuss these risk measures in the framework of stochastic dominance as well as in the framework of coherent risk measures. We derive statements concerning the structure and the stability of the resulting optimization problems whereby we emphasize on models including integrality requirements on some decision variables. Then we propose decomposition algorithms for the mean-risk models under consideration and present numerical results for two stochastic programming applications.

Identiferoai:union.ndltd.org:DUETT/oai:DUETT:duett-04272004-161939
Date09 June 2004
CreatorsMaerkert, Andreas
ContributorsProf. Dr. RĂ¼diger Schultz, Prof. Dr. Maarten H. van der Vlerk
PublisherGerhard-Mercator-Universitaet Duisburg
Source SetsDissertations and other Documents of the Gerhard-Mercator-University Duisburg
LanguageGerman
Detected LanguageEnglish
Typetext
Formatapplication/octet-stream, text/html, application/pdf
Sourcehttp://www.ub.uni-duisburg.de/ETD-db/theses/available/duett-04272004-161939/
Rightsunrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. Hiermit erteile ich der Universitaet Duisburg das nicht-ausschliessliche Recht unter den unten angegebenen Bedingungen, meine Dissertation, Staatsexamens- oder Diplomarbeit, meinen Forschungs- oder Projektbericht zu veroeffentlichen und zu archivieren. Ich behalte das Urheberrecht und das Recht das Dokument zu veroeffentlichen und in anderen Arbeiten weiterzuverwenden.

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