An introduction to the regenerative method for simulation analysis
The purpose of this report is to provide an introduction to the regenerative method for simulation analysis. The simulations are simulations of stochastic systems, i.e., systems with random elements. The regenerative approach leads to a statistical methodology for analyzing the output of those simulations which have the property of 'starting afresh probabilistically' from time to time. The class of such simulations is very large and very important, including simulations of a broad variety of queues and queueing networks, inventory systems, inspection, maintenance, and repair operations, and numerous other situations.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
An Introduction to the Regenerative Method for Simulation Analysis
M.A. Crane,A.J. Lemoine
No preview available - 1977
analyzing the output applied approximate regeneration approximation techniques blocks central limit theorem computed Control Analysis Corporation converges discrete-event simulations exponentially distributed finite future events given in Section IGLEHART independent and identically interarrival inventory example inventory system jackknife method jth cycle lation law of large Markov chain Markov process methodology model of Section modified process number of customers number of cycles number of units observed obtain a confidence obtaining confidence intervals operating units output data period Poisson process post-ordering-decision inventory level probability problem process X(t quantile queueing model queueing simulation queueing system random time clock random variable ratio estimation regeneration points regenerative approach regenerative method regenerative process regenerative simulations repair facility repairman model run length run the simulation sequence simu simulation output Simulation Results simulation run single-server queue Stable Stochastic Systems statistically independent steady-state distribution steady-state parameters stochastic process stochastic simulations stopping rules Suppose trapping interval valid statistical Variance Reduction