Scenarios for Multistage Stochastic Programs

Jitka Dupačová; Giorgio Consigli; Stein W. Wallace

doi:10.1023/a:1019206915174

Scenarios for Multistage Stochastic Programs

Jitka Dupačová, Giorgio Consigli, Stein W. Wallace

Mathematics

Research output: Contribution to journal › Article › peer-review

366 Scopus citations

Abstract

A major issue in any application of multistage stochastic programming is the representation of the underlying random data process. We discuss the case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model. No assumptions on convexity with respect to the random parameters are required. We emphasize the notion of representative scenarios (or a representative scenario tree) relative to the problem being modeled.

Original language	British English
Pages (from-to)	25-53
Number of pages	29
Journal	Annals of Operations Research
Volume	100
Issue number	1-4
DOIs	https://doi.org/10.1023/a:1019206915174
State	Published - 2000

Keywords

Clustering
Importance sampling
Inference and bounds
Matching moments
Problem oriented requirements
Scenarios and scenario trees

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10.1023/a:1019206915174

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abstract = "A major issue in any application of multistage stochastic programming is the representation of the underlying random data process. We discuss the case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model. No assumptions on convexity with respect to the random parameters are required. We emphasize the notion of representative scenarios (or a representative scenario tree) relative to the problem being modeled.",

keywords = "Clustering, Importance sampling, Inference and bounds, Matching moments, Problem oriented requirements, Scenarios and scenario trees",

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AU - Dupačová, Jitka

AU - Consigli, Giorgio

AU - Wallace, Stein W.

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AB - A major issue in any application of multistage stochastic programming is the representation of the underlying random data process. We discuss the case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model. No assumptions on convexity with respect to the random parameters are required. We emphasize the notion of representative scenarios (or a representative scenario tree) relative to the problem being modeled.

KW - Clustering

KW - Importance sampling

KW - Inference and bounds

KW - Matching moments

KW - Problem oriented requirements

KW - Scenarios and scenario trees

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DO - 10.1023/a:1019206915174

M3 - Article

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SP - 25

EP - 53

JO - Annals of Operations Research

JF - Annals of Operations Research

IS - 1-4

ER -

Scenarios for Multistage Stochastic Programs

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Keywords

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