Quotation 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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Publication's profile

Status of publication Published
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)
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