The use of discrete moment bounds in probabilisticconstrained stochastic programming models

András Prékopa

Annals of Operations Research, 1999, vol. 85, issue 0, 38 pages

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

Date: 1999
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DOI: 10.1023/A:1018921811281

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