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Simulation input data modeling

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Abstract

Input data modeling is a critical component of a successful simulation application. A perspective of the area is given with an emphasis on available probability distributions as models, estimation methods, model selection and discrimination, and goodness of fit. Three specific distribution classes (lambda,S B , TES processes) are discussed in some detail to illustrate characteristics that favor input models. Regarding estimation, we argue for maximum likelihood estimation over method of moments and other matching schemes due to intrinsic superior properties (presuming a specific model) and the capability of accommodating messy data types. We conclude with a list of specific research problems and areas warranting additional attention.

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  1. Department of Statistics, University of Central Florida, 32816-2370, Orlando, FL, USA

    Mark E. Johnson

  2. Department of Industrial Engineering and Management Systems, University of Central Florida, 32816-2450, Orlando, FL, USA

    Mansooreh Mollaghasemi

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Johnson, M.E., Mollaghasemi, M. Simulation input data modeling. Ann Oper Res 53, 47–75 (1994). https://doi.org/10.1007/BF02136826

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