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The Recursive Definition of Stochastic Linear Programming Problems within an Algebraic Modeling Language

Author

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  • C.S. Buchanan
  • K.I.M. McKinnon
  • G.K. Skondras

Abstract

Many optimization problems can be expressed naturally in a recursive manner. Problems with a dynamic structure are commonly expressed in this way especially when they are to be solved by dynamic programming. Many stochastic linear programming problems have an underlying Markov structure, and for these a recursive definition is natural. Real-world examples of such problems are often so large that it is not practical to solve them without a modeling language. No existing algebraic modeling language provides a natural way of specifying a model using a dynamic programming recurrence. This paper describes the advantages of recursive model definition for stochastic linear programming problems, and presents the language constructs necessary to implement this within an algebraic modeling language. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • C.S. Buchanan & K.I.M. McKinnon & G.K. Skondras, 2001. "The Recursive Definition of Stochastic Linear Programming Problems within an Algebraic Modeling Language," Annals of Operations Research, Springer, vol. 104(1), pages 15-32, April.
    • Handle: RePEc:spr:annopr:v:104:y:2001:i:1:p:15-32:10.1023/a:1013126632649
    DOI: 10.1023/A:1013126632649
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    Cited by:

    1. Bernhard Hasche & Rüdiger Barth & Derk Jan Swider, 2007. "Effects of Improved Wind Forecasts on Operational Costs in the German Electricity System," Energy and Environmental Modeling 2007 24000017, EcoMod.
    2. 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.
    3. 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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