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Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the sys- tem under consideration. A close match between the in- put model and the true underlying probabilistic mech- anism associated with the system is required for suc- cessful input modeling.
Abstract: General guidelines for selecting probabilistic input models as part of a discrete-event simulation study are presented.
Input modeling techniques for discrete-event simulations - IEEE Xplore
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This example-driven tutorial examines introductory techniques for input modeling. Most simulation texts (e.g., A.M. Law and W.D. Kelton, 2000) have a broader ...
Input modeling for discrete-event simulation. Author: Lawrence M. Leemis ... Input modeling for discrete-event simulation. Pages 16 - 23. PREVIOUS ARTICLE.
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Input Data Management (IDM) is a time consuming and costly process for Discrete Event Simulation (DES) projects. In this paper, a methodology for IDM in DES ...
The model is a set of instructions for generating behavioral data of the form of plots of X(variable of interest) against T(time). The modeling relation, ...
Discrete-event simulation models typically have stoch- astic components that mimic the probabilistic nature of the system under consideration. Successful.
Event modeling lets you perform discrete changes on continuous variables. The two most common applications of event modeling are: Trigger-and-hold mechanism, ...
Home · Grey Literature · Proceedings · Winter Simulation Conference Proceedings · Input Modeling for Discrete-Event Simulation ...
Oct 16, 2021 · Input modeling in simulation aims to help manage uncertainty in data and come up with approximations that represent a system with sufficient accuracy.