IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v69y2023i4p2127-2146.html
   My bibliography  Save this article

Wait Time–Based Pricing for Queues with Customer-Chosen Service Times

Author

Listed:
  • Chen-An Lin

    (Fuqua School of Business, Duke University, Durham, North Carolina, 27708)

  • Kevin Shang

    (Fuqua School of Business, Duke University, Durham, North Carolina, 27708)

  • Peng Sun

    (Fuqua School of Business, Duke University, Durham, North Carolina, 27708)

Abstract

This paper studies a pricing problem for a single-server queue where customers arrive according to a Poisson process. For each arriving customer, the service provider announces a price rate and system wait time. In response, the customer decides whether to join the queue, and, if so, the duration of the service time. The objective is to maximize either the long-run average revenue or social welfare. We formulate this problem as a continuous-time control model whose optimality conditions involve solving a set of delay differential equations. We develop an innovative method to obtain the optimal control policy, whose structure reveals interesting insights. The optimal dynamic price rate policy is not monotone in the wait time. That is, in addition to the congestion effect (the optimal price rate increases in the wait time), we find a compensation effect, meaning that the service provider should lower the price rate when the wait time is longer than a threshold. Compared with the prevalent static pricing policy, our optimal dynamic pricing policy improves the objective value through admission control, which, in turn, increases the utilization of the server. In a numerical study, we find that our revenue-maximizing pricing policy outperforms the best static pricing policy, especially when the arrival rate is low, and customers are impatient. Interestingly, the revenue-maximizing policy also improves social welfare over the static pricing policy in most of the tested cases. We extend our model to consider nonlinear pricing and heterogeneous customers. Nonlinear pricing may improve the revenue significantly, although linear pricing is easier to implement. For the hetergeneous customer case, we obtain similar policy insights as our base model.

Suggested Citation

  • Chen-An Lin & Kevin Shang & Peng Sun, 2023. "Wait Time–Based Pricing for Queues with Customer-Chosen Service Times," Management Science, INFORMS, vol. 69(4), pages 2127-2146, April.
  • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:4:p:2127-2146
    DOI: 10.1287/mnsc.2022.4474
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2022.4474
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2022.4474?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Baric{s} Ata & Shiri Shneorson, 2006. "Dynamic Control of an M/M/1 Service System with Adjustable Arrival and Service Rates," Management Science, INFORMS, vol. 52(11), pages 1778-1791, November.
    2. Motoaki, Yutaka & Shirk, Matthew G., 2017. "Consumer behavioral adaption in EV fast charging through pricing," Energy Policy, Elsevier, vol. 108(C), pages 178-183.
    3. Wallace J. Hopp & Seyed M. R. Iravani & Gigi Y. Yuen, 2007. "Operations Systems with Discretionary Task Completion," Management Science, INFORMS, vol. 53(1), pages 61-77, January.
    4. Albert Y. Ha, 2001. "Optimal Pricing That Coordinates Queues with Customer-Chosen Service Requirements," Management Science, INFORMS, vol. 47(7), pages 915-930, July.
    5. Saed Alizamir & Francis de Véricourt & Peng Sun, 2013. "Diagnostic Accuracy Under Congestion," Management Science, INFORMS, vol. 59(1), pages 157-171, December.
    6. Sabri Çelik & Costis Maglaras, 2008. "Dynamic Pricing and Lead-Time Quotation for a Multiclass Make-to-Order Queue," Management Science, INFORMS, vol. 54(6), pages 1132-1146, June.
    7. Albert Y. Ha, 1998. "Incentive-Compatible Pricing for a Service Facility with Joint Production and Congestion Externalities," Management Science, INFORMS, vol. 44(12-Part-1), pages 1623-1636, December.
    8. Thomas B. Crabill & Donald Gross & Michael J. Magazine, 1977. "A Classified Bibliography of Research on Optimal Design and Control of Queues," Operations Research, INFORMS, vol. 25(2), pages 219-232, April.
    9. Jennifer M. George & J. Michael Harrison, 2001. "Dynamic Control of a Queue with Adjustable Service Rate," Operations Research, INFORMS, vol. 49(5), pages 720-731, October.
    10. Philipp Afèche & J. Michael Pavlin, 2016. "Optimal Price/Lead-Time Menus for Queues with Customer Choice: Segmentation, Pooling, and Strategic Delay," Management Science, INFORMS, vol. 62(8), pages 2412-2436, August.
    11. David W. Low, 1974. "Optimal Dynamic Pricing Policies for an M / M / s Queue," Operations Research, INFORMS, vol. 22(3), pages 545-561, June.
    12. Krishnan S. Anand & M. Faz{i}l Paç & Senthil Veeraraghavan, 2011. "Quality-Speed Conundrum: Trade-offs in Customer-Intensive Services," Management Science, INFORMS, vol. 57(1), pages 40-56, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ying Xu & Alan Scheller-Wolf & Katia Sycara, 2015. "The Benefit of Introducing Variability in Single-Server Queues with Application to Quality-Based Service Domains," Operations Research, INFORMS, vol. 63(1), pages 233-246, February.
    2. Pnina Feldman & Ella Segev, 2022. "The Important Role of Time Limits When Consumers Choose Their Time in Service," Management Science, INFORMS, vol. 68(9), pages 6666-6686, September.
    3. Jinting Wang & Zhongbin Wang & Yunan Liu, 2020. "Reducing Delay in Retrial Queues by Simultaneously Differentiating Service and Retrial Rates," Operations Research, INFORMS, vol. 68(6), pages 1648-1667, November.
    4. Philipp Afèche & Opher Baron & Yoav Kerner, 2013. "Pricing Time-Sensitive Services Based on Realized Performance," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 492-506, July.
    5. Dongyuan Zhan & Amy R. Ward, 2019. "Staffing, Routing, and Payment to Trade off Speed and Quality in Large Service Systems," Operations Research, INFORMS, vol. 67(6), pages 1738-1751, November.
    6. Delasay, Mohammad & Ingolfsson, Armann & Kolfal, Bora & Schultz, Kenneth, 2019. "Load effect on service times," European Journal of Operational Research, Elsevier, vol. 279(3), pages 673-686.
    7. Tom Fangyun Tan & Serguei Netessine, 2014. "When Does the Devil Make Work? An Empirical Study of the Impact of Workload on Worker Productivity," Management Science, INFORMS, vol. 60(6), pages 1574-1593, June.
    8. Michael Freeman & Nicos Savva & Stefan Scholtes, 2017. "Gatekeepers at Work: An Empirical Analysis of a Maternity Unit," Management Science, INFORMS, vol. 63(10), pages 3147-3167, October.
    9. Robert J. Batt & Christian Terwiesch, 2017. "Early Task Initiation and Other Load-Adaptive Mechanisms in the Emergency Department," Management Science, INFORMS, vol. 63(11), pages 3531-3551, November.
    10. Mohammad Delasay & Armann Ingolfsson & Bora Kolfal, 2016. "Modeling Load and Overwork Effects in Queueing Systems with Adaptive Service Rates," Operations Research, INFORMS, vol. 64(4), pages 867-885, August.
    11. Balcıõglu, Barış & Varol, Yãgız, 2022. "Fair and profitable: How pricing and lead-time quotation policies can help," European Journal of Operational Research, Elsevier, vol. 299(3), pages 977-986.
    12. Saed Alizamir & Francis de Véricourt & Peng Sun, 2013. "Diagnostic Accuracy Under Congestion," Management Science, INFORMS, vol. 59(1), pages 157-171, December.
    13. Benioudakis, Myron & Burnetas, Apostolos & Ioannou, George, 2021. "Lead-time quotations in unobservable make-to-order systems with strategic customers: Risk aversion, load control and profit maximization," European Journal of Operational Research, Elsevier, vol. 289(1), pages 165-176.
    14. Xiaojun Liang & Yinghui Tang, 2019. "The improvement upon the reliability of the k-out-of-n:F system with the repair rates differentiation policy," Operational Research, Springer, vol. 19(2), pages 479-500, June.
    15. Saghafian, Soroush & Hopp, Wallace J. & Iravani, Seyed M. R. & Cheng, Yao & Diermeier, Daniel, 2017. "Workload Management in Telemedical Physician Triage and Other Knowledge-Based Service Systems," Working Paper Series rwp17-035, Harvard University, John F. Kennedy School of Government.
    16. Barιş Ata & Deishin Lee & Erkut Sönmez, 2019. "Dynamic Volunteer Staffing in Multicrop Gleaning Operations," Operations Research, INFORMS, vol. 67(2), pages 295-314, March.
    17. Terry A. Taylor, 2018. "On-Demand Service Platforms," Manufacturing & Service Operations Management, INFORMS, vol. 20(4), pages 704-720, October.
    18. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
    19. Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
    20. Shone, Rob & Glazebrook, Kevin & Zografos, Konstantinos G., 2019. "Resource allocation in congested queueing systems with time-varying demand: An application to airport operations," European Journal of Operational Research, Elsevier, vol. 276(2), pages 566-581.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:69:y:2023:i:4:p:2127-2146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.