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Strategic Queueing Behavior and Its Impact on System Performance in Service Systems with the Congestion-Based Staffing Policy

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  • Pengfei Guo

    (Faculty of Business, Hong Kong Polytechnic University, Hung Hom, Hong Kong)

  • Zhe George Zhang

    (Department of Decision Sciences, Western Washington University, Bellingham, Washington 98225; and Beedie School of Business, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada)

Abstract

We study strategic customer behavior in a multiserver stochastic service system with a congestion-based staffing (CBS) policy. With the CBS policy, the number of working servers is dynamically adjusted according to the queue length. Besides lining up for free service, customers have the option of paying a fee and getting faster service. Customers' equilibrium behavior is studied under two information scenarios: In the no information scenario, customers only know the long-term statistics, such as the expected waiting time; in the partial information scenario, customers observe the number of working servers and understand the staffing policy upon their arrival. Unlike a queueing system with a constant staffing level, a positive externality is associated with customers' joining the CBS system. Both avoid-the-crowd and follow-the-crowd customer behaviors are possible, and multiple equilibria could exist. We develop the stationary performance measures of the system by considering the customers' strategic behavior. Numerical analysis shows that information can either hurt or improve the performance of the system, depending on the staffing and pricing policy. Another important conclusion is that the system performance is more robust to setting a relatively high than a relatively low price.

Suggested Citation

  • Pengfei Guo & Zhe George Zhang, 2013. "Strategic Queueing Behavior and Its Impact on System Performance in Service Systems with the Congestion-Based Staffing Policy," Manufacturing & Service Operations Management, INFORMS, vol. 15(1), pages 118-131, September.
    • Handle: RePEc:inm:ormsom:v:15:y:2013:i:1:p:118-131
    DOI: 10.1287/msom.1120.0406
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    Cited by:

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    3. Zhe George Zhang & Xiaoling Yin, 2021. "Designing a Sustainable Two‐Tier Service System with Customer's Asymmetric Preference for Servers," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 3856-3880, November.
    4. Qian, Qu & Zhuang, Weifen, 2017. "Tax/subsidy and capacity decisions in a two-tier health system with welfare redistributive objective," European Journal of Operational Research, Elsevier, vol. 260(1), pages 140-151.
    5. Yu, Mengqiao & Ding, Yichuan & Lindsey, Robin & Shi, Cong, 2016. "A data-driven approach to manpower planning at U.S.–Canada border crossings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 34-47.
    6. Liu, Zhongyi & Liu, Jingchen & Zhai, Xin & Wang, Guanying, 2019. "Police staffing and workload assignment in law enforcement using multi-server queueing models," European Journal of Operational Research, Elsevier, vol. 276(2), pages 614-625.
    7. Jiaqi Zhou & Ilya O. Ryzhov, 2021. "Equilibrium analysis of observable express service with customer choice," Queueing Systems: Theory and Applications, Springer, vol. 99(3), pages 243-281, December.
    8. Zhang, Zhe George & Yin, Xiaoling, 2021. "Information and pricing effects in two-tier public service systems," International Journal of Production Economics, Elsevier, vol. 231(C).
    9. Philipp Afèche & Opher Baron & Joseph Milner & Ricky Roet-Green, 2019. "Pricing and Prioritizing Time-Sensitive Customers with Heterogeneous Demand Rates," Operations Research, INFORMS, vol. 67(4), pages 1184-1208, July.
    10. Du, Shaofu & Peng, Jing & Nie, Tengfei & Yu, Yugang, 2020. "Pricing strategies and mechanism choice in reward-based crowdfunding," European Journal of Operational Research, Elsevier, vol. 284(3), pages 951-966.
    11. Hu, Shu & Zhu, Stuart X. & Fu, Ke, 2023. "Optimal trade-in and refurbishment strategies for durable goods," European Journal of Operational Research, Elsevier, vol. 309(1), pages 133-151.
    12. A. Wang & I. Ziedins, 2018. "Probabilistic selfish routing in parallel batch and single-server queues," Queueing Systems: Theory and Applications, Springer, vol. 88(3), pages 389-407, April.
    13. Abdoun Sylia & Taleb Samira, 2024. "Strategic Joining in an Unobservable Markovian Queue with Differentiated Vacations," SN Operations Research Forum, Springer, vol. 5(3), pages 1-27, September.
    14. Lingjiao Zhang & Jinting Wang & Yilin Wang, 2023. "Strategic Behavior and Optimization of an M/M/1 Queue with N-Policy and Hysteretic Control," Methodology and Computing in Applied Probability, Springer, vol. 25(4), pages 1-29, December.
    15. Hanukov, Gabi & Avinadav, Tal & Chernonog, Tatyana & Yechiali, Uri, 2019. "Performance improvement of a service system via stocking perishable preliminary services," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1000-1011.

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