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By M. A. Crane, A. J. Lemoine (eds.)

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In this section we shall cast all such "regenerative processes" into a coannon framework and then give a simple technique for obtaining a confidence interval~ based on the simulation output~ for a variety of steady-state system parameters of practical interest. The method covers any discrete-event simu- lation that can be modeled as a regenerative process. A discrete-event simu- lation is one in which the state of the system being simulated only changes at a discrete, but possibly random~ set of time points.

Now suppose we begin the simulation assuming we have a "like-new system," that is, I0 units are placed into operation at time 0 and there are 5 new 25 units in the spare pool. Define state is entered, where we set Rk as the RI m 0 kth time that this "like-new" That is, the by the times that the repair facility becomes empty. Rk'S are defined Note, at each of these times there are i0 operating units and 5 fully repaired units in the spare pool. Furthermore~ because of the "lack of memory" property of the failure times, the operating units behave identically as they behaved when they were new.

Inventory example of Section 2, regeneration occurs at time falls in the set if ((7, J):J = O, I, . I. n Dn • For the ( & , Dn) Xn is equal A more general example of this sort will be given As these and other examples will show s the class of regenera- tive simulations is very broad indeed. Let [%s J ~ I} ~j = ~j + 1 - ~j for j ~ 1 . Note that the "sojourn times" between consecutive epochs of regeneration are independent and identically distributed. We will assume henceforth that E{~ I) < ~ not a restrictive assumption.

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