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Evaluation of scenario-generation methods for stochastic programming

Kaut, Michal and Wallace, Stein W (2007) Evaluation of scenario-generation methods for stochastic programming. Pacific Journal of Optimization, 3 (2). pp. 257-271.

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Stochastic programs can only be solved with discrete distributions of limited cardinality. Input, however, normally comes in the form of continuous distributions or large data sets. Creating a limited discrete distribution from input is called scenario generation. In this paper, we discuss how to evaluate the quality or suitability of scenario generation methods for a given stochastic programming model. We formulate minimal requirements that should be imposed on a scenario generation method before it can be used for solving the stochastic programming model. We also show how the requirements can be tested. The procedures for testing a scenario generation method is illustrated on a case from portfolio management.

Item Type: Journal Article
Journal or Publication Title: Pacific Journal of Optimization
Uncontrolled Keywords: stochastic programming ; scenario tree ; scenario generation ; stability
Departments: Lancaster University Management School > Management Science
ID Code: 45403
Deposited By: ep_importer_pure
Deposited On: 11 Jul 2011 19:31
Refereed?: Yes
Published?: Published
Last Modified: 14 Mar 2018 00:06
Identification Number:

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