Grouping Observations in Digital Simulation

This paper presents a method for deriving a confidence interval for a population mean from the output of a simulation run. The method groups the observations on a run into batches and uses these batches as the basic data for analysis. The technique is not new. What is new is the procedure for determining how to group the observations into batches that satisfy certain assumptions necessary for the technique to work correctly. It is inexpensive and requires a moderate knowledge of statistics. The results of testing the method on a single server queuing model with Poisson distributed arrivals of exponentially distributed service times (M/M/1), indicate that the proposed technique performs as theory suggests for moderate activity levels. However, for higher activity levels performance is below theoretical expectation for small sample sizes n. As n increases, performance converges to expectation. Moreover, two calculations of the sample sizes needed to obtain results with moderate accuracy indicate that these sample sizes are in a range where the procedure is expected to perform with small error.

• 저널Management Science · 24(5) · 510–521
• 토픽Simulation Techniques and Applications · Management Science and Operations Research
• DOI10.1287/mnsc.24.5.510
• 저자George S. Fishman