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Scenario Optimization for Multi-Stage Stochastic Programming Problems Hochreiter, Ronald Stochastic programming scenario generation facility location financial engineering The field 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 infinite-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 stochastic process. In the first part of the talk we take a look at scenario optimization from the viewpoint of a decision taker, to provide rather non-technical insights into the problem. In the second part of the talk we examplify scenario tree generation by reviewing one specific algorithm based on multi-dimensional facility location applying backward stagewise clustering. An example from the area of financial engineering concludes the talk. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ronald Hochreiter 2005 Is Part Of Dagstuhl Seminar Proceedings, Volume 5031, Algorithms for Optimization with Incomplete Information (2005) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.05031.26 urn:nbn:de:0030-drops-614 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05031.26 eng https://creativecommons.org/licenses/by/4.0/legalcode