SEVEN HABITS OF HIGHLY SUCCESSFUL INPUT MODELERS

Larry Leemis
Department of Mathematics
College of William and Mary
Williamsburg, VA 23187-8795
e-mail: leemis@math.wm.edu


Abstract:

Discrete-event simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The general question considered here is how to model an element (e.g., arrival process, service times) in a discrete-event simulation given a data set collected on the element of interest. For brevity, it is assumed that data is available on the aspect of the simulation of interest. It is also assumed that raw data is available, as opposed to censored data, grouped data, or summary statistics.

Seven factors to consider for selecting probabilistic input models for a discrete-event simulation study are presented:

Most simulation texts (e.g., Law and Kelton 1991) have a broader treatment of input modeling than presented here. Nelson et al. (1995) survey advanced techniques.

Link to paper.