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Evaluation of appointment scheduling rules: A multi-performance measurement approach

Author

Listed:
  • Stefan Creemers

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Marc Lambrecht
  • Jeroen Beliën
  • Maud van den Broeke

Abstract

Appointment scheduling rules are used to determine when a customer is to receive service during a service session. In general, appointment scheduling rules do not consider the sequencing of individual customers, but provide simple guidelines on how to assign appointment times to a set of (arriving) customers. Many appointment scheduling rules exist and are being used in practice (e.g., in healthcare and legal services). Which appointment scheduling rule is best, however, is still an open question. In order to answer this question, we develop an analytical model that allows to assess the performance (in terms of customer waiting time, server idle time, and server overtime) of appointment scheduling rules in a wide variety of settings. More specifically, the model takes into account: (1) customer unpunctuality, (2) no-shows, (3) service interruptions, and (4) delay in session start time. In addition, we allow the use of general distributions to capture system processes. We adopt an efficient algorithm (with respect to computational and memory requirements) to assess the performance of 314 scheduling rules and use data envelopment analysis to identify the rules that have good, robust performance in a wide variety of settings.

Suggested Citation

  • Stefan Creemers & Marc Lambrecht & Jeroen Beliën & Maud van den Broeke, 2021. "Evaluation of appointment scheduling rules: A multi-performance measurement approach," Post-Print hal-03140243, HAL.
    • Handle: RePEc:hal:journl:hal-03140243
    DOI: 10.1016/j.omega.2020.102231
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    Citations

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    Cited by:

    1. Nossack, Jenny, 2022. "Therapy scheduling and therapy planning at hospitals," Omega, Elsevier, vol. 109(C).
    2. Agnihothri, Saligrama & Cappanera, Paola & Nonato, Maddalena & Visintin, Filippo, 2024. "Appointment scheduling in surgery pre-admission testing clinics," Omega, Elsevier, vol. 123(C).
    3. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).

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