Discrete-event System Simulation
This text provides a basic treatment of discrete-event simulation, one of the most widely used operations research tools presently available. Proper collection and analysis of data, use of analytic techniques, verification and validation of models, and an appropriate design of simulation experiments are treated extensively. Readily understandable to those having a basic familiarity with differential and integral calculus, probability theory and elementary statistics. The Second Edition reorganises, updates and expands coverage to reflect the most recent developments.
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analysis approximately arrival rate assumed assumptions autocorrelation average number batch means called Chapter checkout CLOCK common random numbers confidence interval Consider conveyor correlated sampling defined delay determine discrete distributed with mean downtime entities equation Erlang distribution Example exponentially distributed FORTRAN gamma distribution given histogram hour hypothesis initial conditions interarrival inventory Kolmogorov-Smirnov test long-run machine material handling method minutes normal distribution number of customers object observations occur operation output data parameters performance point estimator Poisson distribution Poisson process probability queue length queueing system random digits random numbers random variable rejected response ri ri ri run length sample mean scheduled Section sequence server utilization shown in Figure SIMAN SIMSCRIPT II.5 simulation analyst simulation languages simulation model single-server queue standard deviation statistical steady-state subroutine Table technique triangular distribution uniformly distributed validation variance variates waiting line Weibull distribution
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