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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:

- collecting the right data
- using the full range of input models
- performing a complete statistical analysis
- evaluating time dependence
- considering parametric vs. nonparametric approaches
- considering tail behavior
- performing a sensitivity analysis.

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.