A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs
Optimization problems involving uncertain data arise in many areas of industrial and economic applications. Stochastic programming provides a useful framework for modeling and solving optimization problems for which a probability distribution of the unkno
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Dissertation von Dipl.-Math. Debora Mahlke aus Haan
Referent: Prof. Dr. A. Martin Korreferent: Prof. Dr. R. Schultz Tag der Einreichung: 11. Dezember 2009 Tag der m¨ undlichen Pr¨ ufung: 23. Februar 2010 Darmstadt 2010 D 17
Debora Mahlke A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs
VIEWEG+TEUBNER RESEARCH Stochastic Programming Editor: Prof. Dr. Rüdiger Schultz
Uncertainty is a prevailing issue in a growing number of optimization problems in science, engineering, and economics. Stochastic programming offers a flexible methodology for mathematical optimization problems involving uncertain parameters for which probabilistic information is available. This covers model formulation, model analysis, numerical solution methods, and practical implementations. The series ”Stochastic Programming“ presents original research from this range of topics.
Debora Mahlke
A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs With Application in Energy Production
VIEWEG+TEUBNER RESEARCH
Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.
Dissertation Technische Universität Darmstadt, 2010 D 17
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Acknowledgments First of all, I would like to thank all those people who have helped and supported me during the completion of this work. Especially, I would like to express my gratitude to my advisor Professor Alexander Martin for giving me the opportunity to carry out my research work in his group. Besides his continuous support and guidance, he encouraged me to follow my own ideas and enabled me to attend many conferences where I could present my work. Furthermore, I am grateful to my co-referee Professor R¨ udiger Schultz for providing a second opinion and for the motivating support of my research in the field of Stochastic Optimization. Special thanks go to my colleague and friend Andrea Zelmer for the numerous motivating discussions and the intensive coll
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