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Interval Coverage in Multiclass Queues Using Batch Mean Estimates

Author

Listed:
  • M. Eric Johnson

    (Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203)

  • John Jackman

    (Department of Industrial Engineering, Iowa State University, Ames, Iowa 50011)

Abstract

We investigate the fixed, sample-size, batch-mean procedure for creating confidence intervals from simulated data obtained from a stochastic queueing system with multiple customer classes. We show that, for a multiclass M/M/1 queue, serial correlation between customers of the same class decreases to zero as the number of customer classes increases. We also derive a closed-form expression for the asymptotic variance of waiting time by customer type. We then empirically examine batch-mean estimator coverage for a simple queue with multiple customer classes. We find that batch-mean estimators perform better in terms of coverage and interval half width in multiclass queues, with a fixed number of observations per class, than in the traditionally studied single-class systems. We also examine the effect of multiple classes where the total computational effort remains fixed.

Suggested Citation

  • M. Eric Johnson & John Jackman, 1996. "Interval Coverage in Multiclass Queues Using Batch Mean Estimates," Management Science, INFORMS, vol. 42(12), pages 1744-1752, December.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:12:p:1744-1752
    DOI: 10.1287/mnsc.42.12.1744
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    Cited by:

    1. Andrei Sleptchenko & M. Eric Johnson, 2015. "Maintaining Secure and Reliable Distributed Control Systems," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 103-117, February.

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