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Comparisons of ticket and standard queues

Author

Listed:
  • Otis B. Jennings
  • Jamol Pender

    (Cornell University)

Abstract

Upon arrival to a ticket queue, a customer is offered a slip of paper with a number on it—indicating the order of arrival to the system—and is told the number of the customer currently in service. The arriving customer then chooses whether to take the slip or balk, a decision based on the perceived queue length and associated waiting time. Even after taking a ticket, a customer may abandon the queue, an event that will be unobservable until the abandoning customer would have begun service. In contrast, a standard queue has a physical waiting area so that abandonment is apparent immediately when it takes place and balking is based on the actual queue length at the time of arrival. We prove heavy traffic limit theorems for the generalized ticket and standard queueing processes, discovering that the processes converge together to the same limit, a regulated Ornstein–Uhlenbeck process. One conclusion is that for a highly utilized service system with a relatively patient customer population, the ticket and standard queue performances are asymptotically indistinguishable on the scale typically uncovered under heavy traffic approaches. Next, we heuristically estimate several performance metrics of the ticket queue, some of which are of a sensitivity typically undetectable under diffusion scaling. The estimates are tested using simulation and are shown to be quite accurate under a general collection of parameter settings.

Suggested Citation

  • Otis B. Jennings & Jamol Pender, 2016. "Comparisons of ticket and standard queues," Queueing Systems: Theory and Applications, Springer, vol. 84(1), pages 145-202, October.
  • Handle: RePEc:spr:queues:v:84:y:2016:i:1:d:10.1007_s11134-016-9493-y
    DOI: 10.1007/s11134-016-9493-y
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    References listed on IDEAS

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    1. J. E. Reed & Amy R. Ward, 2008. "Approximating the GI/GI/1+GI Queue with a Nonlinear Drift Diffusion: Hazard Rate Scaling in Heavy Traffic," Mathematics of Operations Research, INFORMS, vol. 33(3), pages 606-644, August.
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    Cited by:

    1. Jamol Pender & Richard Rand & Elizabeth Wesson, 2020. "A Stochastic Analysis of Queues with Customer Choice and Delayed Information," Mathematics of Operations Research, INFORMS, vol. 45(3), pages 1104-1126, August.
    2. Kwangji Kim & Mi-Jung Kim & Jae-Kyoon Jun, 2020. "Small Queuing Restaurant Sustainable Revenue Management," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    3. Gabi Hanukov & Shoshana Anily & Uri Yechiali, 2020. "Ticket queues with regular and strategic customers," Queueing Systems: Theory and Applications, Springer, vol. 95(1), pages 145-171, June.
    4. Gabi Hanukov & Michael Hassoun & Oren Musicant, 2021. "On the Benefits of Providing Timely Information in Ticket Queues with Balking and Calling Times," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    5. Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.
    6. Li Xiao & Susan H. Xu & David D. Yao & Hanqin Zhang, 2022. "Optimal staffing for ticket queues," Queueing Systems: Theory and Applications, Springer, vol. 102(1), pages 309-351, October.
    7. Kaan Kuzu & Long Gao & Susan H. Xu, 2019. "To Wait or Not to Wait: The Theory and Practice of Ticket Queues," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 853-874, October.
    8. Baris Ata & Peter W. Glynn & Xiaoshan Peng, 2017. "An equilibrium analysis of a discrete-time Markovian queue with endogenous abandonments," Queueing Systems: Theory and Applications, Springer, vol. 86(1), pages 141-212, June.

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