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The use of discrete moment bounds in probabilisticconstrained stochastic programming models

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  • András Prékopa

Abstract

In the past few years, efficient methods have been developed for bounding probabilitiesand expectations concerning univariate and multivariate random variables based on theknowledge of some of their moments. Closed form as well as algorithmic lower and upperbounds of this type are now available. The lower and upper bounds are frequently closeenough even if the number of utilized moments is relatively small. This paper shows howthe probability bounds can be incorporated in probabilistic constrained stochastic programmingmodels in order to obtain approximate solutions for them in a relatively simple way. Copyright Kluwer Academic Publishers 1999

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  • András Prékopa, 1999. "The use of discrete moment bounds in probabilisticconstrained stochastic programming models," Annals of Operations Research, Springer, vol. 85(0), pages 21-38, January.
    • Handle: RePEc:spr:annopr:v:85:y:1999:i:0:p:21-38:10.1023/a:1018921811281
    DOI: 10.1023/A:1018921811281
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    Cited by:

    1. Song, Haiqing & Cheung, Raymond K. & Wang, Haiyan, 2014. "An arc-exchange decomposition method for multistage dynamic networks with random arc capacities," European Journal of Operational Research, Elsevier, vol. 233(3), pages 474-487.

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