Multivariate composite distributions for coefficients in synthetic optimization problems

    Authors

    R. R. Hill;C. H. Reilly

    Abbreviated Journal Title

    Eur. J. Oper. Res.

    Keywords

    correlation; composition; joint probability distribution; GENERALIZED ASSIGNMENT PROBLEM; SINGLE-MACHINE; ALGORITHM; MINIMIZE; Management; Operations Research & Management Science

    Abstract

    In most cases, coefficients in synthetic optimization problems are randomly generated based on specified univariate marginal distributions. Additionally, the various types of coefiicients are assumed to be mutually independent, even though coefficients in practical problems may be correlated. In this paper, multivariate composite distributions with specified marginal distributions and a specified Pearson product-moment population correlation structure are characterized. The generation of synthetic optimization problems is the principal motivation for characterizing these composite distributions. but they are also useful for many other simulation applications. Type L composite distributions are composed of the extreme-correlation distributions for a multivariate random variable only, while Type U composite distributions are based on the extreme-correlation distributions and the joint distribution under independence. Closed-form composition probabilities for distributions of trivariate random variables are presented. Methods for identifying correlation structures that are amenable to representation by composite distributions are discussed. (C) 2000 Elsevier Science B.V. All rights reserved.

    Journal Title

    European Journal of Operational Research

    Volume

    121

    Issue/Number

    1

    Publication Date

    1-1-2000

    Document Type

    Article

    Language

    English

    First Page

    64

    Last Page

    77

    WOS Identifier

    WOS:000084502500006

    ISSN

    0377-2217

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