Using genetic algorithms and an indifference-zone ranking and selection procedure under common random numbers for simulation optimisation

    Authors

    D. Nazzal; M. Mollaghasemi; H. Hedlund;A. Bozorgi

    Comments

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    Abbreviated Journal Title

    J. Simul.

    Keywords

    genetic algorithms; ranking and selection; simulation optimisation; MULTIPLE COMPARISONS; Computer Science, Interdisciplinary Applications; Operations Research &; Management Science

    Abstract

    Genetic algorithms (GAs) are one of the many optimisation methodologies that have been used in conjunction with simulation modelling. The most critical step with a GA is the assignment of the selective probabilities to the alternatives. Selective probabilities are assigned based on the alternatives' estimated performances which are obtained using simulation. An accurate estimate should be obtained to reduce the number of cases in which the search is oriented towards the wrong direction. Furthermores, it is important to obtain this estimate without many replications. This study proposes a simulation optimisation methodology that combines the GA and an indifference-zone (IZ) ranking and selection procedure under common random numbers (CRN). By using an IZ procedure, a statistical guarantee can be made about the direction in which the search should progress as well as a statistical guarantee about the results from the search. Furthermore, using CRN significantly reduces the required number of replications. Journal of Simulation (2012) 6, 56-66. doi:10.1057/jos.2011.14; published online 22 July 2011

    Journal Title

    Journal of Simulation

    Volume

    6

    Issue/Number

    1

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    56

    Last Page

    66

    WOS Identifier

    WOS:000313664200007

    ISSN

    1747-7778

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