Advances in Decision Sciences

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Volume 2006 |Article ID 087514 | https://doi.org/10.1155/JAMDS/2006/87514

Mahyar Amouzegar, Khosrow Moshirvaziri, "A simulation framework for networked queue models: Analysis of queue bounds in a G/G/c supply chain", Advances in Decision Sciences, vol. 2006, Article ID 087514, 13 pages, 2006. https://doi.org/10.1155/JAMDS/2006/87514

A simulation framework for networked queue models: Analysis of queue bounds in a G/G/c supply chain

Received04 Apr 2006
Accepted18 May 2006
Published18 Sep 2006

Abstract

Some limited analytical derivation for networked queue models has been proposed in the literature, but their solutions are often of a great mathematical challenge. To overcome such limitations, simulation tools that can deal with general networked queue topology must be developed. Despite certain limitations, simulation algorithms provide a mechanism to obtain insight and good numerical approximation to parameters of networked queues. This paper presents a closed stochastic simulation network model and several approximation and bounding schemes for G/G/c systems. The analysis was originally conducted to verify the integrity of simulation models used to develop alternative policy options conducted on behalf of the US Air Force. We showed that the theoretical bounds could be used to approximate mean capacities at various queues. In this paper, we present results for a G/G/8 system though similar results have been obtained for other networks of queues as well.

References

  1. M. Amouzegar, L. S. Galway, and A. Geller, “Supporting expeditionary aerospace forces: an analysis of jet engine intermediate maintenance options,” Tech. Rep. MR-1431-AF, RAND, California, 2001. View at: Google Scholar
  2. S. L. Brumelle, “Some inequalities for parallel-server queues,” Operations Research, vol. 19, pp. 402–413, 1971. View at: Google Scholar | Zentralblatt MATH | MathSciNet
  3. B. Diamond, S. Lamperti, D. Krahl, and A. Nastasi, “Extend v6 User's Guide,” 2002. View at: Google Scholar
  4. D. Gross and C. M. Harris, Fundamentals of Queueing Theory, Wiley Series in Probability and Statistics: Texts and References Section, John Wiley & Sons, New York, 3rd edition, 1998. View at: Google Scholar | Zentralblatt MATH | MathSciNet
  5. J. F. C. Kingman, “On queues in heavy traffic,” Journal of the Royal Statistical Society. Series B. Methodological, vol. 24, pp. 383–392, 1962. View at: Google Scholar | Zentralblatt MATH | MathSciNet
  6. L. Köllerström, “Heavy traffic theory for queues with several servers: I,” Journal of Applied Probability, vol. 11, pp. 544–552, 1974. View at: Google Scholar | Zentralblatt MATH | MathSciNet
  7. R. Larson and A. Odoni, Urban Operations Research, Prentice-Hall, New Jersey, 1981. View at: Google Scholar
  8. W. G. Marchal, “Some simpler bounds on the mean queuing time,” Operations Research, vol. 26, no. 6, pp. 1083–1088, 1978. View at: Google Scholar | Zentralblatt MATH | MathSciNet
  9. K. T. Marshall, “Some inequalities in queuing,” Operations Research, vol. 16, pp. 651–665, 1968. View at: Google Scholar | Zentralblatt MATH | MathSciNet

Copyright © 2006 Mahyar Amouzegar and Khosrow Moshirvaziri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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