| Description |
1 online resource (xvi, 182 pages) |
| Series |
Vieweg+Teubner research. Stochastic programming |
|
Vieweg+Teubner research. Stochastic programming.
|
| Summary |
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 |
| Analysis |
wiskunde |
|
mathematics |
|
toegepaste wiskunde |
|
applied mathematics |
|
Mathematics (General) |
|
Wiskunde (algemeen) |
| Notes |
Diss.-- Technische Universität Darmstadt, 2010 |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Stochastic programming.
|
|
Programación estocástica
|
|
Stochastic programming
|
| Genre/Form |
dissertations.
|
|
Academic theses
|
|
Academic theses.
|
|
Thèses et écrits académiques.
|
| Form |
Electronic book
|
| ISBN |
9783834898296 |
|
3834898295 |
|
9783834814098 |
|
3834814091 |
|
