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
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
Discrete-event simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Output interpretation: some myths and common errors in simulation experiments
Proceedings of the 33nd conference on Winter simulation
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Sports analogy for modelling of combat in the air domain
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation metamodels for modeling output distribution parameters
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Simulation and optimization in a health center in Medellin, Colombia
Proceedings of the 40th Conference on Winter Simulation
A methodology for input data management in discrete event simulation projects
Proceedings of the 40th Conference on Winter Simulation
Multi-echelon supply chain simulation using metamodel
Proceedings of the 40th Conference on Winter Simulation
Introduction to simulation input modeling
Proceedings of the Winter Simulation Conference
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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, Fishman 2001) have a broader treatment of input modeling than presented here. Nelson and Yamnitsky (1998) survey advanced techniques.