Performance extrapolation in discrete-event systems simulation

The use of simulation as an engineering tool to design complex computer stochastic systems is often inhibited by cost. Extensive computer processing is needed to recompute performance functions for changes in input parameters. Moreover, simulation models are often subject to errors caused by the input data in estimating the parameters of the input distributions. The 'what if' analysis is needed to establish confidence in a model's validity with respect to small changes in the system's parameters. To solve the 'what if' problem for several scenarios requires a separate simulation run for each scenario. A method to estimate the performance for several scenarios using a single simulation run based on the efficient score function is developed. The approximate function is an exponential random function which is tangential to the performance function in expectation. We study the algorithm's properties and examine the validity of the estimates based on this single run procedure by performing some experimental studies on some simple queueing and pert models. Implementation 'details' necessary to package this method with existing simulation software are provided. Finally, there is a set of recommended directions for future research.