Introduction to modeling and generating probabilistic input processes for simulation

Authors:
Michael E. Kuhl;Emily K. Lada;Natalie M. Steiger;Mary Ann Wagner;James R. Wilson
Affiliations:
Rochester Institute of Technology, Rochester, NY;SAS Institute Inc., Cary, NC;University of Maine, Orono, ME;SAIC, Vienna, VA;North Carolina State University, Raleigh, NC
Venue:
Proceedings of the 40th Conference on Winter Simulation
Year:
2008

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Abstract

Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. Many of the references include live hyperlinks providing online access to the referenced material.