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Making Sense of Output and Increasing Efficiency

  • George S. Fishman
Chapter
Part of the Springer Series in Operations Research book series (ORFE)

Abstract

A simulation run customarily generates sample-path data on user-specified dynamic processes that evolve as simulated time elapses. These often include delay time, queue length, and resource utilization. Sample averages summarize these data and usually become the focal point of reports and presentations to management. Since they merely approximate corresponding long-run averages that would be observed if the simulation were run for an infinite rather than a finite amount of time, some measure of error should accompany each sample average. Confidence intervals, standard errors, and relative errors provide three alternative means of assessing how well sample averages approximate true long-run averages. As illustration, the Final Tableau in Figure 6.12a displays 99 percent confidence intervals under the headings Lower and Upper, standard error under sqrt[B*W(L, B)/t], and relative error under (Upper — Lower)/\(\left| {\bar X} \right|\). A report that omits an error assessment can lead to serious misconceptions of how well sample averages characterize unknown long-run averages.

Keywords

Sample Average Variance Reduction Interval Length Exceedance Probability Percent Confidence Interval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • George S. Fishman
    • 1
  1. 1.Department of Operations ResearchUniversity of North Carolina at Chapel HillChapel HillUSA

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