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Queueing models for appointment-driven systems

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  • S. Creemers

    (UMR CNRS 8179 - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • M. Lambrecht

Abstract

Many service systems are appointment-driven. In such systems, customers make an appointment and join an external queue (also referred to as the “waiting list”). At the appointed date, the customer arrives at the service facility, joins an internal queue and receives service during a service session. After service, the customer leaves the system. Important measures of interest include the size of the waiting list, the waiting time at the service facility and server overtime. These performance measures may support strategic decision making concerning server capacity (e.g. how often, when and for how long should a server be online). We develop a new model to assess these performance measures. The model is a combination of a vacation queueing system and an appointment system. Copyright Springer Science+Business Media, LLC 2010
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • S. Creemers & M. Lambrecht, 2010. "Queueing models for appointment-driven systems," Post-Print hal-00800195, HAL.
    • Handle: RePEc:hal:journl:hal-00800195
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    References listed on IDEAS

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    1. Marc R. Lambrecht & Philip L. Ivens & Nico J. Vandaele, 1998. "ACLIPS: A Capacity and Lead Time Integrated Procedure for Scheduling," Management Science, INFORMS, vol. 44(11-Part-1), pages 1548-1561, November.
    2. Martin A. Lariviere & Jan A. Van Mieghem, 2004. "Strategically Seeking Service: How Competition Can Generate Poisson Arrivals," Manufacturing & Service Operations Management, INFORMS, vol. 6(1), pages 23-40, January.
    3. Chrwan-Jyh Ho & Hon-Shiang Lau, 1992. "Minimizing Total Cost in Scheduling Outpatient Appointments," Management Science, INFORMS, vol. 38(12), pages 1750-1764, December.
    4. Gabriel R. Bitran & Devanath Tirupati, 1991. "Approximations for Networks of Queues with Overtime," Management Science, INFORMS, vol. 37(3), pages 282-300, March.
    5. Ho, Chrwan-Jyh & Lau, Hon-Shiang, 1999. "Evaluating the impact of operating conditions on the performance of appointment scheduling rules in service systems," European Journal of Operational Research, Elsevier, vol. 112(3), pages 542-553, February.
    6. Naishuo Tian & Zhe George Zhang, 2006. "Vacation Queueing Models Theory and Applications," International Series in Operations Research and Management Science, Springer, number 978-0-387-33723-4, September.
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    Cited by:

    1. Chen, Shih-Pin, 2016. "Time value of delays in unreliable production systems with mixed uncertainties of fuzziness and randomness," European Journal of Operational Research, Elsevier, vol. 255(3), pages 834-844.
    2. Tugba Cayirli & Pinar Dursun & Evrim D. Gunes, 2019. "An integrated analysis of capacity allocation and patient scheduling in presence of seasonal walk-ins," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 524-561, June.
    3. Izady, Navid, 2019. "An integrated approach to demand and capacity planning in outpatient clinics," European Journal of Operational Research, Elsevier, vol. 279(2), pages 645-656.
    4. Navid Izady, 2015. "Appointment Capacity Planning in Specialty Clinics: A Queueing Approach," Operations Research, INFORMS, vol. 63(4), pages 916-930, August.
    5. Creemers, Stefan & Beliën, Jeroen & Lambrecht, Marc, 2012. "The optimal allocation of server time slots over different classes of patients," European Journal of Operational Research, Elsevier, vol. 219(3), pages 508-521.
    6. Creemers, Stefan & Lambrecht, Marc R. & Beliën, Jeroen & Van den Broeke, Maud, 2021. "Evaluation of appointment scheduling rules: A multi-performance measurement approach," Omega, Elsevier, vol. 100(C).

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