Introduction to modeling and generating probabilistic input processes for simulation

Techniques are presented for modeling, fitting, and generating many of the univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Be¿zier distribution family because of the flexibility of these families to model a wide range of distributional shapes that arise in practical applications. Also discussed are nonparametric and semiparametric 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. The applications range from pharmaceutical manufacturing and medical decision analysis to smart-materials research and healthcare systems analysis. Many of the references include live hyperlinks providing online access to the referenced material.