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Evaluation of scenario-generation methods for stochastic programming

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>2007
<mark>Journal</mark>Pacific Journal of Optimization
Issue number2
Number of pages15
Pages (from-to)257-271
Publication StatusPublished
<mark>Original language</mark>English


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.