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Stability analysis of a two-station cascade queueing network

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Abstract

We consider a two-station cascade network, where the first station has Poisson input and the second station has renewal input, with i.i.d. service times at both stations. The following partial interaction exists between stations: whenever the second station becomes empty while customers are awaiting service at the first one, one customer can jump to the second station to be served there immediately. However, the first station cannot assist the second one in the opposite case. For this system, we establish necessary and sufficient stability conditions of the basic workload process, using a regenerative method. An extension of the basic model, including a multiserver first station, a different service time distribution for customers jumping from station 1 to station 2, and an arbitrary threshold d 1≥1 on the queue-size at station 1 allowing jumps to station 2, are also treated.

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References

  • Agnihothri, S. R., Mishra, A. K., & Simmons, D. E. (2003). Workforce cross-training decisions in field service systems with two job types. The Journal of the Operational Research Society, 54(4), 410–418.

    Article  Google Scholar 

  • Ahghari, M., & Balcioglu, B. (2009). Benefits of cross-training in a skill-based routing contact center with priority queues and impatient customers. IIE Transactions, 41, 524–536.

    Article  Google Scholar 

  • Ahn, H.-S., Duenyas, I., & Zhang, Q. R. (2004). Optimal control of a flexible server. Advances in Applied Probability, 36, 139–170.

    Article  Google Scholar 

  • Andradottir, S., Ayhan, H., & Down, G. D. (2003). Dynamic server allocation for queueing networks with flexible servers. Operations Research, 51(6), 952–968.

    Article  Google Scholar 

  • Asmussen, S. (2002). Applied probability and queues. Berlin: Springer.

    Google Scholar 

  • Avrachenkov, K., & Morozov, E. (2010). Stability analysis of GI/G/c/K retrial queue with constant retrial rate. Sci. Report No. 7335, INRIA.

  • Balter, M. H., Li, C., Osogami, T., Scheller-Wolf, A., & Squillante, M. S. (2003). Cycle stealing under immediate dispatch task assignment. In Proceedings of the 23rd international conference on distributed computing systems (ICDS’03) (pp. 274–285).

    Google Scholar 

  • Bell, S. L., & Williams, R. J. (1999). Dynamic scheduling of a server system with two parallel servers: asymptotic optimality of a continuous review threshold policy in heavy traffic. In Proceedings of the 38 conference on decision and control, Phoenix, AZ, Dec. 1999 (pp. 2255–2260).

    Google Scholar 

  • Bell, S. L., & Williams, R. J. (2001). Dynamic scheduling of a system with two parallel servers in heavy traffic with complete resource pooling: Asymptotic optimality of a continuous review threshold policy. Annals of Probability, 11, 608–649.

    Article  Google Scholar 

  • Borovkov, A. (1965). Some limit theorems in the queueing theory II. Theory of Probability and Its Applications, 10, 375–400.

    Article  Google Scholar 

  • Bramson, M. (2008). Stability of queueing networks. Probability Surveys, 5, 169–345.

    Article  Google Scholar 

  • Chen, H. (1995). Fluid approximation and stability of multiclass queueing networks: work-conserving disciplines. The Annals of Applied Probability, 5, 637–665.

    Article  Google Scholar 

  • Dai, J. (1995a). On positive Harris recurrence of multiclass queueing networks: a unified approach via fluid limit models. The Annals of Applied Probability, 5, 49–77.

    Article  Google Scholar 

  • Dai, J. (1995b). Stability of open multiclass queueing networks via fluid models. In F. Kelly & R. Williams (Eds.), Stochastic networks (pp. 71–90). New York: Springer.

    Google Scholar 

  • Dai, J. G., Hasenbein, J. J., & Kim, B. (2007). Stability of join-the-shortest-queue networks. Queueing Systems, 57, 129–145.

    Article  Google Scholar 

  • Dimakis, A., & Walrand, J. (2006). Sufficient conditions for stability of longest-queue-first scheduling: second order properties using fluid limits. Advances in Applied Probability, 38, 505–521.

    Article  Google Scholar 

  • Down, D. G., & Lewis, M. E. (2006). Dynamic load balancing in parallel queueing systems: stability and optimal control. European Journal of Operational Research, 168, 509–519.

    Article  Google Scholar 

  • Feller, W. (1971). An introduction to probability theory and its applications (Vol. II). New York: Wiley.

    Google Scholar 

  • Foley, R. D., & McDonald, D. R. (2005). Large deviations of a modified Jackson network: stability and rough asymptotics. The Annals of Applied Probability, 15(1B), 519–541.

    Article  Google Scholar 

  • Foss, S., & Konstantopoulos, T. (2004). An overview on some stochastic stability methods. Journal of the Operations Research Society of Japan, 47(4), 275–303.

    Google Scholar 

  • Harrison, M., & Lopez, M. J. (1999). Heavy traffic resource pooling in parallel-server systems. Queueing Systems, 33, 339–368.

    Article  Google Scholar 

  • Iglehart, D., & Whitt, W. (1970). Multiple channel queues in heavy traffic. Advances in Applied Probability, 2, 150–170.

    Article  Google Scholar 

  • Kirkizlar, E., Andradottir, S., & Ayhan, H. (2010). Robustness of efficient server assignment policies to service time distributions in finite-buffered lines. Naval Research Logistics, 57(6), 563–582.

    Article  Google Scholar 

  • Mandelbaum, A., & Stolyar, A. L. (2004). Scheduling flexible servers with convex delay costs: heavy-traffic optimality of the generalized -rule. Operations Research, 52(6), 836–855.

    Article  Google Scholar 

  • Morozov, E. (1997). The tightness in the ergodic analysis of regenerative queueing processes. Queueing Systems, 27, 179–203.

    Article  Google Scholar 

  • Morozov, E. (2002). Instability of nonhomogeneous queueing networks. Journal of Mathematical Sciences, 112(2), 4155–4167.

    Article  Google Scholar 

  • Morozov, E. (2004). Weak regeneration in modeling of queueing processes. Queueing Systems, 46, 295–315.

    Article  Google Scholar 

  • Morozov, E. (2007a). Coupling and monotonicity of queues. Sci. report No. 779, CRM. http://www.crm.cat.

  • Morozov, E. (2007b). A multiserver retrial queue: regenerative stability analysis. Queueing Systems, 56, 157–168.

    Article  Google Scholar 

  • Morozov, E. (2010). A general multiserver state-dependent queueing system. Sci. report No 27, CRM. http://www.crm.cat.

  • Morozov, E., & Delgado, R. (2009). Stability analysis of regenerative queues. Automation and Remote Control, 70(12), 1977–1991.

    Article  Google Scholar 

  • Müller, A., & Stoyan, D. (2000). Comparisons methods for stochastic models. New York: Wiley.

    Google Scholar 

  • Sigman, K. (1990). One-dependent regenerative processes and queues in continuous time. Mathematics of Operations Research, 15, 175–189.

    Article  Google Scholar 

  • Sigman, K., & Thorrisson, H. (1994). A note on the existence of regeneration times. Journal of Applied Probability, 31, 1116–1122.

    Article  Google Scholar 

  • Sigman, K., & Wolff, R. W. (1993). A review of regenerative processes. SIAM Review, 35, 269–288.

    Article  Google Scholar 

  • Smith, W. L. (1955). Regenerative stochastic processes. Proceedings of the Royal Society of Edinburgh. Section A, 232, 6–31.

    Google Scholar 

  • Stolyar, A. L., & Tezcan, T. (2010). Control of systems with flexible multi-server pools: a shadow routing approach. Queueing Systems, 66, 1–51.

    Article  Google Scholar 

  • Tekin, E., Hopp, W. J., & Van Oyen, M. P. (2009). Pooling strategies for call center agent cross-training. IIE Transactions, 41(6), 546–561.

    Article  Google Scholar 

  • Terekhov, D., & Beck, J. C. (2009). An extended queueing control model for facilities with front room and back room operations and mixed-skilled workers. European Journal of Operational Research, 198(1), 223–231.

    Article  Google Scholar 

  • Tezcan, T. (2009). Augmented fluid models and stability of queuing systems. https://netfiles.uiuc.edu/ttezcan/www/afm070809.pdf.

  • Tezcan, T. (2010). Stability analysis of N-model systems under a static priority rule using augmented fluid models. https://netfiles.uiuc.edu/ttezcan/www/afm011510.pdf.

  • Tezcan, T., & Dai, J. G. (2010). Dynamic control of N-systems with many servers: asymptotic optimality of a static priority policy in heavy traffic. Operations Research, 58(1), 94–110.

    Article  Google Scholar 

  • Tsai, Y. C., & Argon, N. T. (2008). Dynamic server assignment policies for assembly-type queues with flexible servers. Naval Research Logistics, 55(3), 234–251.

    Article  Google Scholar 

  • Vlasiou, M., & Boxma, O. J. (2007). On queues with service and interarrival times depending on waiting times. Queueing Systems, 56(3–4), 121–132.

    Google Scholar 

  • Wang, Z., & Xing, W. (2008). Performance of service policies in a specialized service system with parallel servers. Annals of Operations Research, 159, 459–460.

    Article  Google Scholar 

  • Whitt, W. (2002). Stochastic-process limits. Berlin: Springer.

    Google Scholar 

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Acknowledgements

The authors thank the anonymous referees for very useful comments which have led to considerable improvement of the paper. Special thanks also to Rosario Delgado for a valuable discussion of the fluid approach.

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Correspondence to E. Morozov.

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The work of E. Morozov is supported by the Russian Foundation for Basic Research under grant 10-07-00017.

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Morozov, E., Steyaert, B. Stability analysis of a two-station cascade queueing network. Ann Oper Res 202, 135–160 (2013). https://doi.org/10.1007/s10479-011-1034-9

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