Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
Simulating Stable Stochastic Systems, I: General Multiserver Queues
Journal of the ACM (JACM)
Simulating Stable Stochastic Systems, II: Markov Chains
Journal of the ACM (JACM)
Achieving specific accuracy in simulation output analysis
Communications of the ACM
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
An Introduction to the Regenerative Method for Simulation Analysis
An Introduction to the Regenerative Method for Simulation Analysis
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In many simulation studies a large amount of time and money is spent on model development, but little effort is made to analyze the simulation output data in a proper manner. Since most simulation models use random variables as input, the output data are themselves random and care must therefore be taken in drawing conclusions about the system under study. In this paper we present an up-to-date treatment of procedures which can be used for constructing confidence intervals for measures of performance of a simulated system. The emphasis will be on simple, easy-to-use procedures which have been shown to perform well in practice.