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The effects of the availability of waiting-time information on a balking queue

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  • Guo, Pengfei
  • Zipkin, Paul

Abstract

We consider two balking queue models with different types of information about delays. Potential customers arrive according to a Poisson process, and they decide whether to stay or balk based on the available delay information. In the first model, an arriving customer learns a rough range of the current queue length. In the second model, each customer's service time is the sum of a geometric number of i.i.d. exponential phases, and an arriving customer learns the total number of phases remaining in the system. For each information model, we compare two systems, identical except that one has more precise information. In many cases, better information increases throughput and thus benefits the service provider. But this is not always so. The effect depends on the shape of the distribution describing customers' sensitivities to delays. We also study the effects of information on performance as seen by customers. Again, more information is often good for customers, but not always.

Suggested Citation

  • Guo, Pengfei & Zipkin, Paul, 2009. "The effects of the availability of waiting-time information on a balking queue," European Journal of Operational Research, Elsevier, vol. 198(1), pages 199-209, October.
  • Handle: RePEc:eee:ejores:v:198:y:2009:i:1:p:199-209
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    References listed on IDEAS

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    Cited by:

    1. Rouba Ibrahim, 2018. "Sharing delay information in service systems: a literature survey," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 49-79, June.
    2. Olga Bountali & Antonis Economou, 2019. "Equilibrium threshold joining strategies in partially observable batch service queueing systems," Annals of Operations Research, Springer, vol. 277(2), pages 231-253, June.
    3. Apostolos Burnetas & Antonis Economou & George Vasiliadis, 2017. "Strategic customer behavior in a queueing system with delayed observations," Queueing Systems: Theory and Applications, Springer, vol. 86(3), pages 389-418, August.
    4. Ming Hu & Yang Li & Jianfu Wang, 2018. "Efficient Ignorance: Information Heterogeneity in a Queue," Management Science, INFORMS, vol. 64(6), pages 2650-2671, June.
    5. Jianfu Wang & Ming Hu, 2020. "Efficient Inaccuracy: User-Generated Information Sharing in a Queue," Management Science, INFORMS, vol. 66(10), pages 4648-4666, October.
    6. Antonis Economou, 2022. "How much information should be given to the strategic customers of a queueing system?," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 421-423, April.
    7. Dimitrios Logothetis & Antonis Economou, 2023. "The impact of information on transportation systems with strategic customers," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2189-2206, July.
    8. Canbolat, Pelin G., 2020. "Bounded rationality in clearing service systems," European Journal of Operational Research, Elsevier, vol. 282(2), pages 614-626.
    9. Guo, Pengfei & Sun, Wei & Wang, Yulan, 2011. "Equilibrium and optimal strategies to join a queue with partial information on service times," European Journal of Operational Research, Elsevier, vol. 214(2), pages 284-297, October.
    10. Hassin, Refael & Roet-Green, Ricky, 2018. "Cascade equilibrium strategies in a two-server queueing system with inspection cost," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1014-1026.
    11. Shiliang Cui & Senthil Veeraraghavan, 2016. "Blind Queues: The Impact of Consumer Beliefs on Revenues and Congestion," Management Science, INFORMS, vol. 62(12), pages 3656-3672, December.
    12. Gopinath Panda & Veena Goswami & Abhijit Datta Banik, 2016. "Equilibrium and Socially Optimal Balking Strategies in Markovian Queues with Vacations and Sequential Abandonment," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(05), pages 1-34, October.
    13. S. Srivatsa Srinivas & Rahul R. Marathe, 2020. "Equilibrium in a finite capacity M/M/1 queue with unknown service rates consisting of strategic and non-strategic customers," Queueing Systems: Theory and Applications, Springer, vol. 96(3), pages 329-356, December.
    14. Shone, Rob & Knight, Vincent A. & Williams, Janet E., 2013. "Comparisons between observable and unobservable M/M/1 queues with respect to optimal customer behavior," European Journal of Operational Research, Elsevier, vol. 227(1), pages 133-141.
    15. Olga Boudali & Antonis Economou, 2013. "The effect of catastrophes on the strategic customer behavior in queueing systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(7), pages 571-587, October.

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