Multivariate statistical simulation
Multivariate statistical simulation
A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Parameter estimation of the Weibull probability distribution
Mathematics and Computers in Simulation
Using univariate Be´zier distributions to model simulation input processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Modeling dependencies in stochastic simulation inputs
Proceedings of the 29th conference on Winter simulation
Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
Least squares estimation of nonhomogeneous Poisson processes
Proceedings of the 30th conference on Winter simulation
Input modeling using a computer algebra system
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Simulation of manufacturing systems
Applied system simulation
Input modeling using quantile statistical methods
WSC '04 Proceedings of the 36th conference on Winter simulation
Modeling tool failures in semiconductor FAB simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
A simulation model of a helicopter ambulance service
WSC '05 Proceedings of the 37th conference on Winter simulation
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Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. 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. This example-driven tutorial examines introductory techniques for input modeling. Most simulation texts (e.g., Law and Kelton 2000) have a broader treatment of input modeling than presented here. Nelson and Yamnitsky (1998) survey advanced techniques.