Truncation rules in simulation analysis : effect of batch size, time scale and input distribution on the application of Schriber's rule Public Deposited

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  • The objective of many simulations is to study the steady-state behavior of a nonterminating system. The initial conditions of the system are often atypical because of the complexity of the system. Simulators often start the simulation with the system empty and idle, and truncate, or delete, some quantity of the initial observations to reduce the initialization bias. This paper studies the application of Schriber's truncation rule to a queueing model, and the effects of parameter selection. Schriber's rule requires the simulator to select the parameters of batch size, number of batches, and a measure of precision. In addition, Schriber's rule assumes the output is a time series of discrete observations. Previous studies of Schriber's rule have not considered the effect of variation in the time scale (time between observations). The performance measures for comparison are the mean squared error and the half-length of the confidence interval. The results indicate that the time scale and batch size are significant parameters, and that the number of batches has little effect on the output. A change in the distribution of service time did not alter the results. In addition, it was determined that multiple replicates should be used in establishing the truncation point instead of a single run, and the simulator should carefully consider the choice of time scale for the output series and the batch size.
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