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Closed Queuing Systems with Exponential Servers

Author

Listed:
  • William J. Gordon

    (General Motors Research Laboratories, Warren, Michigan)

  • Gordon F. Newell

    (University of California, Richmond, California)

Abstract

The results contained herein pertain to the problem of determining the equilibrium distribution of customers in closed queuing systems composed of M interconnected stages of service. The number of customers, N , in a closed queuing system is fixed since customers pass repeatedly through the M stages with neither entrances nor exits permitted. At the i th stage there are r i parallel exponential servers all of which have the same mean service rate μ i . When service is completed at stage i , a customer proceeds directly to stage j with probability p ıj . Such closed systems are shown to be stochastically equivalent to open systems in which the number of customers cannot exceed N . The equilibrium equations for the joint probability distribution of customers are solved by a separation of variables technique. In the limit of N → ∞ it is found that the distribution of customers in the system is regulated by the stage (or stages) with the slowest effective service rate. Asymptotic expressions are given for the marginal distributions of customers in such systems. Then, an asymptotic analysis is carried out for systems with a large number of stages (i.e., M ≫ 1) all of which have comparable effective service rates. Approximate expressions are obtained for the marginal probability distributions. The details of the analysis are illustrated by an example.

Suggested Citation

  • William J. Gordon & Gordon F. Newell, 1967. "Closed Queuing Systems with Exponential Servers," Operations Research, INFORMS, vol. 15(2), pages 254-265, April.
  • Handle: RePEc:inm:oropre:v:15:y:1967:i:2:p:254-265
    DOI: 10.1287/opre.15.2.254
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    Cited by:

    1. Sahba, Pedram & BalcIog[small tilde]lu, BarIs, 2011. "The impact of transportation delays on repairshop capacity pooling and spare part inventories," European Journal of Operational Research, Elsevier, vol. 214(3), pages 674-682, November.
    2. Santiago R. Balseiro & David B. Brown & Chen Chen, 2021. "Dynamic Pricing of Relocating Resources in Large Networks," Management Science, INFORMS, vol. 67(7), pages 4075-4094, July.
    3. Valeriy A. Naumov & Yuliya V. Gaidamaka & Konstantin E. Samouylov, 2019. "On Two Interacting Markovian Queueing Systems," Mathematics, MDPI, vol. 7(9), pages 1-12, September.
    4. Shuji Kijima & Tomomi Matsui, 2008. "Randomized approximation scheme and perfect sampler for closed Jackson networks with multiple servers," Annals of Operations Research, Springer, vol. 162(1), pages 35-55, September.
    5. Xiaoju Zhang & Huijuan Li & Meng Wu, 2022. "Optimization of Resource Allocation in Automated Container Terminals," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    6. Kang, Seungmo & Medina, Juan C. & Ouyang, Yanfeng, 2008. "Optimal operations of transportation fleet for unloading activities at container ports," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 970-984, December.
    7. Nico Dijk & Barteld Schilstra, 2022. "On two product form modifications for finite overflow systems," Annals of Operations Research, Springer, vol. 310(2), pages 519-549, March.
    8. Zhang, Xiaoju & Zeng, Qingcheng & Sheu, Jiuh-Biing, 2019. "Modeling the productivity and stability of a terminal operation system with quay crane double cycling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 181-197.
    9. J. P. van der Gaast & M. B. M. de Koster & I. J. B. F. Adan, 2018. "Conveyor Merges in Zone Picking Systems: A Tractable and Accurate Approximate Model," Service Science, INFORMS, vol. 52(6), pages 1428-1443, December.
    10. Daisik Nam & Minyoung Park, 2020. "Improving the Operational Efficiency of Parcel Delivery Network with a Bi-Level Decision Making Model," Sustainability, MDPI, vol. 12(19), pages 1-19, September.
    11. Manuel Alberto M. Ferreira & Marina Andrade & José António Filipe & Manuel Pacheco Coelho, 2011. "Statistical Queuing Theory with Some Applications," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 1(4), pages 190-190.
    12. Tuǧrul Dayar & Akın Meriç, 2008. "Kronecker representation and decompositional analysis of closed queueing networks with phase-type service distributions and arbitrary buffer sizes," Annals of Operations Research, Springer, vol. 164(1), pages 193-210, November.
    13. Jonatha Anselmi & Bernardo D'Auria & Neil Walton, 2013. "Closed Queueing Networks Under Congestion: Nonbottleneck Independence and Bottleneck Convergence," Mathematics of Operations Research, INFORMS, vol. 38(3), pages 469-491, August.
    14. Choi, S. H. & Lee, J. S. L., 2001. "Computational algorithms for modeling unreliable manufacturing systems based on Markovian property," European Journal of Operational Research, Elsevier, vol. 133(3), pages 667-684, September.

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