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.

Full text not available from this repository.


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:
ID Code:
Deposited By:
Deposited On:
11 Jul 2011 18:31
Last Modified:
21 Nov 2022 21:24