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This paper shows that a previously developed technique for analyzing simulations of GI/G/s queues and Markov chains applies to discrete-event simulations that can be modeled as regenerative processes. It is possible to address questions of simulation run duration and of starting and stopping simulations because of the existence of a random grouping of observations that produces independent identically distributed blocks in the course of the simulation. This grouping allows one to obtain confidence intervals for a general function of the steady-state distribution of the process being simulated and for the asymptotic cost per unit time. The technique is illustrated with a simulation of a retail inventory distribution system.
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