8.7 Summary

This chapter described many of the statistical aspects of simulation that you will typically encounter in performing a simulation study. An important aspect of performing a correct simulation analysis is to understand the type of data associated with your performance measures (time-based versus observation-based) and how to collect/analyze such data. Then in your modeling you will be faced with specifying the time horizon of your simulation. Most situations involve finite-horizons, which are fortunately easy to analyze via the method of replications. This allows a random sample to be formed across replications and to analyze the simulation output via traditional statistical techniques.

In the case of infinite horizon simulations, things are more complicated. You must first analyze the effect of any warm up period on the performance measures and decide whether you should use the method of replication-deletion or the method of batch means.

Since you often want to use simulation to make a recommendation concerning a design configuration, an analysis across system configurations must be carefully planned. When performing your analysis, you should consider how and when to use the method of common random numbers and you should consider the impact of common random numbers on how you analyze the simulation results.

Now that you have a solid understanding of how to program and model using the JSL and how to analyze your results, you are ready to explore the application of the JSL to additional modeling situations involving more complicated systems. The next chapter concentrates on queueing systemss. These systems form the building blocks for modeling more complicated systems in manufacturing, transportation, and service industries.