Evaluation of scenario-generation methods for stochastic programming

Management Science

Keywords

stochastic programming , scenario tree , scenario generation , stability

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Evaluation of scenario-generation methods for stochastic programming. / Kaut, Michal; Wallace, Stein W.
In: Pacific Journal of Optimization, Vol. 3, No. 2, 2007, p. 257-271.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Author

Kaut, Michal ; Wallace, Stein W. / Evaluation of scenario-generation methods for stochastic programming. In: Pacific Journal of Optimization. 2007 ; Vol. 3, No. 2. pp. 257-271.

Bibtex

@article{6839435f85d84650ac64702dfb02ccfb,

title = "Evaluation of scenario-generation methods for stochastic programming",

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.",

keywords = "stochastic programming , scenario tree , scenario generation , stability",

author = "Michal Kaut and Wallace, {Stein W}",

year = "2007",

language = "English",

volume = "3",

pages = "257--271",

journal = "Pacific Journal of Optimization",

publisher = "Yokohama Publications",

number = "2",

}

RIS

TY - JOUR

T1 - Evaluation of scenario-generation methods for stochastic programming

AU - Kaut, Michal

AU - Wallace, Stein W

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - stochastic programming

KW - scenario tree

KW - scenario generation

KW - stability

M3 - Journal article

VL - 3

SP - 257

EP - 271

JO - Pacific Journal of Optimization

JF - Pacific Journal of Optimization

IS - 2

ER -

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