Scenario Optimization for Multi-Stage Stochastic Programming Problems

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

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.
Original languageEnglish
Title of host publicationAlgorithms for Optimization with Incomplete Information
Editors Susanne Albers and Rolf H. Möhring and Georg Ch. Pflug and Rüdiger Schultz
Place of PublicationVolume 05031 of Dagstuhl Seminar Proceedings
PublisherIBFI, Schloss Dagstuhl, Germany
Pages61 - 63
Volume05031
Publication statusPublished - 1 Dec 2005

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