Hochreiter, Ronald. 2005. Scenario Optimization for Multi-Stage Stochastic Programming Problems. In Algorithms for Optimization with Incomplete Information, Hrsg. Susanne Albers and Rolf H. Möhring and Georg Ch. Pflug and Rüdiger Schultz, 61-63. Volume 05031 of Dagstuhl Seminar Proceedings: IBFI, Schloss Dagstuhl, Germany.
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Abstract
The ¯eld of multi-stage stochastic programming provides a rich modelling framework to tackle a broad range of real-world decision problems. In order to numerically solve such programs - once they get reasonably large - the in¯nite-dimensional optimization problem has to be discretized. The stochastic optimization program generally consists of an optimization model and a stochastic model. In the multi-stage case the stochastic model is most commonly represented as a multi-variate stochastic process. The most common technique to calculate an useable discretization is to generate a scenario tree from the underlying sto- chastic process. Scenario tree generation is exampli¯ed by reviewing one speci¯c algorithm based on multi-dimensional facility location applying backward stagewise clustering.
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Status of publication | Published |
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Affiliation | WU |
Type of publication | Contribution to conference proceedings |
Language | English |
Title | Scenario Optimization for Multi-Stage Stochastic Programming Problems |
Title of whole publication | Algorithms for Optimization with Incomplete Information |
Editor | Susanne Albers and Rolf H. Möhring and Georg Ch. Pflug and Rüdiger Schultz |
Page from | 61 |
Page to | 63 |
Location | Volume 05031 of Dagstuhl Seminar Proceedings |
Publisher | IBFI, Schloss Dagstuhl, Germany |
Year | 2005 |
URL | http://drops.dagstuhl.de/volltexte/2005/61/pdf/05031.HochreiterRonald.ExtAbstract.61.pdf |
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- Hochreiter, Ronald (Details)
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- Institute for Statistics and Mathematics IN (Details)