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Stationary analysis of a single queue with remaining service time-dependent arrivals

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Abstract

We study a generalization of the M / G / 1 system (denoted by rM / G / 1) with independent and identically distributed service times and with an arrival process whose arrival rate \(\lambda _0f(r)\) depends on the remaining service time r of the current customer being served. We derive a natural stability condition and provide a stationary analysis under it both at service completion times (of the queue length process) and in continuous time (of the queue length and the residual service time). In particular, we show that the stationary measure of queue length at service completion times is equal to that of a corresponding M / G / 1 system. For \(f > 0\), we show that the continuous time stationary measure of the rM / G / 1 system is linked to the M / G / 1 system via a time change. As opposed to the M / G / 1 queue, the stationary measure of queue length of the rM / G / 1 system at service completions differs from its marginal distribution under the continuous time stationary measure. Thus, in general, arrivals of the rM / G / 1 system do not see time averages. We derive formulas for the average queue length, probability of an empty system and average waiting time under the continuous time stationary measure. We provide examples showing the effect of changing the reshaping function on the average waiting time.

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Notes

  1. In [5], \(S_k\) denotes the inter-jump times of the PDP; here they denote the successive times when the process hits the boundary of its state space.

References

  1. Akşin, O.Z., Armony, M., Mehrotra, V.: The modern call-center: a multi-disciplinary perspective on operations management research. Prod. Oper. Manag. 16, 665–688 (2007)

    Google Scholar 

  2. Asmussen, S.: Applied Probability and Queues, vol. 51. Springer, Berlin (2008)

    Google Scholar 

  3. Baccelli, F., Brémaud, P.: Palm Probabilities and Stationary Queues, vol. 41. Springer, Berlin (2012)

    Google Scholar 

  4. Bekker, R., Borst, S.C., Boxma, O.J., Kella, O.: Queues with workload-dependent arrival and service rates. Queueing Syst. 46(3–4), 537–556 (2004)

    Article  Google Scholar 

  5. Davis, M.H.A.: Markov Models & Optimization, vol. 49. CRC Press, Boca Raton (1993)

    Book  Google Scholar 

  6. Dshalalow, J.: On single-server closed queues with priorities and state dependent parameters. Queueing Syst. 8(1), 237–253 (1991)

    Article  Google Scholar 

  7. Gans, N., Koole, G., Mandelbaum, A.: Telephone call centers: tutorial, review, and research prospects. Manuf. Serv. Oper. Manag. 5, 73–141 (2003)

    Article  Google Scholar 

  8. Kleinrock, L.: Queueing Systems, Theory, vol. I. Wiley, Hoboken (1975)

    Google Scholar 

  9. Knessl, C., Matkowsky, B.J., Schuss, Z., Tier, C.: Busy period distribution in state-dependent queues. Queueing Syst. 2(3), 285–305 (1987)

    Article  Google Scholar 

  10. Knessl, C., Matkowsky, B.J., Schuss, Z., Tier, C.: A Markov-modulated M/G/1 queue i: stationary distribution. Queueing Syst. 1(4), 355–374 (1987)

    Article  Google Scholar 

  11. Knessl, C., Matkowsky, B.J., Schuss, Z., Tier, C.: A Markov-modulated M/G1 queue ii: busy period and time for buffer overflow. Queueing Syst. 1(4), 375–399 (1987)

    Article  Google Scholar 

  12. Kumar, D., Zhang, L., Tantawi, A.: Enhanced inferencing: estimation of a workload dependent performance model. In: Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools, p. 47. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2009)

  13. Legros, B., Jouini, O., Koole, G.: Optimal scheduling in call centers with a callback option. Perform. Eval. 95, 1–40 (2016)

    Article  Google Scholar 

  14. Little, J.D.C., Graves, S.C.: Little’s law. In: Chhajed, D., Lowe, T.J. (eds.) Building Intuition: Insights from Basic Operations Management Models and Principles, pp. 81–100. Springer (2008)

  15. Lund, R.B., Meyn, S.P., Tweedie, R.L., et al.: Computable exponential convergence rates for stochastically ordered Markov processes. Ann. Appl. Probab. 6(1), 218–237 (1996)

    Article  Google Scholar 

  16. Meyn, S.P., Tweedie, R.L.: Markov Chains and Stochastic Stability, 2nd edn. Cambridge University Press, Cambridge (2012)

    Google Scholar 

  17. Pang, G., Perry, O.: A logarithmic safety staffing rule for contact centers with call blending. Manag. Sci. 61(1), 73–91 (2014)

    Article  Google Scholar 

  18. Perry, D., Stadje, W., Zacks, S., et al.: A duality approach to queues with service restrictions and storage systems with state-dependent rates. J. Appl. Probab. 50(3), 612–631 (2013)

    Article  Google Scholar 

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Legros, B., Sezer, A.D. Stationary analysis of a single queue with remaining service time-dependent arrivals. Queueing Syst 88, 139–165 (2018). https://doi.org/10.1007/s11134-017-9552-z

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  • DOI: https://doi.org/10.1007/s11134-017-9552-z

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