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Language Constructs for Modeling Stochastic Linear Programs

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  • Robert Entriken

Abstract

This paper introduces two tokens of syntax for algebraic modeling languages that are sufficient to model a wide variety of stochastic optimization problems. The tokens are used to declare random parameters and to define a precedence relationship between different random events. It is necessary to make a number of assumptions in order to use this syntax, and it is through these assumptions, which cover the areas of model structure, time, replication, and stages, that we can attain a deeper understanding of this class of problem. Copyright Kluwer Academic Publishers 2001

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  • Robert Entriken, 2001. "Language Constructs for Modeling Stochastic Linear Programs," Annals of Operations Research, Springer, vol. 104(1), pages 49-66, April.
    • Handle: RePEc:spr:annopr:v:104:y:2001:i:1:p:49-66:10.1023/a:1013182717628
    DOI: 10.1023/A:1013182717628
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    Cited by:

    1. Christian Valente & Gautam Mitra & Mustapha Sadki & Robert Fourer, 2009. "Extending Algebraic Modelling Languages for Stochastic Programming," INFORMS Journal on Computing, INFORMS, vol. 21(1), pages 107-122, February.
    2. Robert Fourer & Leo Lopes, 2009. "StAMPL: A Filtration-Oriented Modeling Tool for Multistage Stochastic Recourse Problems," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 242-256, May.

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