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Convexity Results for the Erlang Delay and Loss Formulae When the Server Utilization Is Held Constant

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  • Arie Harel

    (Zicklin School of Business, Baruch College, City University of New York, New York, New York 10010)

Abstract

This paper proves a long-standing conjecture regarding the optimal design of the M / M / s queue. The classical Erlang delay formula is shown to be a convex function of the number of servers when the server utilization is held constant. This means that when the server utilization is held constant, the marginal decrease in the probability that all servers are busy in the M / M / s queue brought about by the addition of two extra servers is always less than twice the decrease brought about by the addition of one extra server. As a consequence, a method of marginal analysis yields the optimal number of servers that minimize the waiting and service costs when the server utilization is held constant. In addition, it is shown that the expected number of customers in the queue and in the system, as well as the expected waiting time and sojourn in the M / M / s queue, are convex in the number of servers when the server utilization is held constant. These results are useful in design studies involving capacity planning in service operations. The classical Erlang loss formula is also shown to be a convex function of the number of servers when the server utilization is held constant.

Suggested Citation

  • Arie Harel, 2011. "Convexity Results for the Erlang Delay and Loss Formulae When the Server Utilization Is Held Constant," Operations Research, INFORMS, vol. 59(6), pages 1420-1426, December.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:6:p:1420-1426
    DOI: 10.1287/opre.1110.0957
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xuelu & Wang, Jinting & Do, Tien Van, 2015. "Threshold properties of the M/M/1 queue under T-policy with applications," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 284-301.
    2. Refael Hassin & Yair Y. Shaki & Uri Yovel, 2015. "Optimal service‐capacity allocation in a loss system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(2), pages 81-97, March.
    3. Nilay Tanık Argon & Sigrún Andradóttir, 2017. "Pooling in tandem queueing networks with non-collaborative servers," Queueing Systems: Theory and Applications, Springer, vol. 87(3), pages 345-377, December.
    4. J. Smith, 2015. "Optimal workload allocation in closed queueing networks with state dependent queues," Annals of Operations Research, Springer, vol. 231(1), pages 157-183, August.

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