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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Abstract

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:
/dk/atira/pure/subjectarea/asjc/2600/2606
Subjects:
ID Code:
45403
Deposited By:
Deposited On:
11 Jul 2011 18:31
Refereed?:
Yes
Published?:
Published
Last Modified:
09 Sep 2020 01:09