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About this book
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 unknown parameters is available.
Motivated by practical optimization problems occurring in energy systems with regenerative energy supply, Debora Mahlke formulates and analyzes multistage stochastic mixed-integer models. For their solution, the author proposes a novel decomposition approach which relies on the concept of splitting the underlying scenario tree into subtrees. Based on the formulated models from energy production, the algorithm is computationally investigated and the numerical results are discussed.
Motivated by practical optimization problems occurring in energy systems with regenerative energy supply, Debora Mahlke formulates and analyzes multistage stochastic mixed-integer models. For their solution, the author proposes a novel decomposition approach which relies on the concept of splitting the underlying scenario tree into subtrees. Based on the formulated models from energy production, the algorithm is computationally investigated and the numerical results are discussed.
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About the author
Debora Mahlke received her Ph.D. in Mathematics from the Technische Universität Darmstadt where she currently works as a postdoctoral research associate.
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Bibliographic Information
Book Title: A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs
Book Subtitle: With Application in Energy Production
Authors: Debora Mahlke
Series Title: Stochastic Programming
DOI: https://doi.org/10.1007/978-3-8348-9829-6
Publisher: Vieweg+Teubner Verlag Wiesbaden
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011
Softcover ISBN: 978-3-8348-1409-8Published: 27 October 2010
eBook ISBN: 978-3-8348-9829-6Published: 30 January 2011
Series ISSN: 2945-8773
Series E-ISSN: 2945-8781
Edition Number: 1
Number of Pages: XVI, 182
Number of Illustrations: 14 b/w illustrations
Topics: Probability Theory and Stochastic Processes, Mathematics, general