ITo measure the performance of this system, we will look at estimates of three quantities. First, we will estimate the expected average delay in queue of die n customers completing their delays during the simul_ation; we denote this quantity by d(nJOn a given run of the simulation (or, for that matter, on a given run of the actual system the simulation model represents), the actual average delay observed of the n customers depends on the interarrival and service-time random variable observations that happen to have been obtained. On another run of the simulation (or on a different day for the real system) there would probably be arrivals at different times, and the service times required would also be different; this would give rise to a different value for the average of the n delays. Thus, the average delay on a given run of the simulation is properly regarded as a random variable itself. What we want to estimate, d(n), is the expected value of this random variable. One interpretation of this is that d(n) is the average of a large (actually, infinite) number of n-customer average delays. From a single run of the simulation resulting in customer delays D" D0 , ••• ,Dn , an obvious estimator of d(n) is