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Using client-variance information to improve dynamic appointment scheduling performance

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  • Rohleder, Thomas R.
  • Klassen, Kenneth J.

Abstract

Clients of services expect short waiting times and servers desire short periods of non-productive time. One of the areas where this is most important is appointment scheduling systems. Recent research has indicated that using information about clients' service time variability can simultaneously reduce waiting times and server idle time. In this study, a more realistic, dynamic appointment-scheduling environment is developed and used to analyze several scheduling rules. Additional complexities considered in this study include: continuously distributed service-time variances, special client appointment requests, and appointment-scheduler uncertainty. Results show that rules using client-variance information are still best at minimizing waiting time and idle time with the additional complexities. In fact, these rules perform best when client variance is large. However, on measures related to clients requesting specific appointment slots the results are not as clear cut. A key factor for these measures is the distribution of the desired slots. When the desired slots are near the end of the appointment scheduling period, traditional rules like first-call-first-appointment perform better on client appointment request measures.

Suggested Citation

  • Rohleder, Thomas R. & Klassen, Kenneth J., 2000. "Using client-variance information to improve dynamic appointment scheduling performance," Omega, Elsevier, vol. 28(3), pages 293-302, June.
    • Handle: RePEc:eee:jomega:v:28:y:2000:i:3:p:293-302
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    References listed on IDEAS

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    1. Chrwan-Jyh Ho & Hon-Shiang Lau, 1992. "Minimizing Total Cost in Scheduling Outpatient Appointments," Management Science, INFORMS, vol. 38(12), pages 1750-1764, December.
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    Cited by:

    1. Xiuli Qu & Yidong Peng & Nan Kong & Jing Shi, 2013. "A two-phase approach to scheduling multi-category outpatient appointments – A case study of a women’s clinic," Health Care Management Science, Springer, vol. 16(3), pages 197-216, September.
    2. Kemper, Benjamin & Klaassen, Chris A.J. & Mandjes, Michel, 2014. "Optimized appointment scheduling," European Journal of Operational Research, Elsevier, vol. 239(1), pages 243-255.
    3. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    4. Pan, Xingwei & Geng, Na & Xie, Xiaolan & Wen, Jing, 2020. "Managing appointments with waiting time targets and random walk-ins," Omega, Elsevier, vol. 95(C).
    5. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    6. Siyun Yu & Vidyadhar G. Kulkarni & Vinayak Deshpande, 2020. "Appointment Scheduling for a Health Care Facility with Series Patients," Production and Operations Management, Production and Operations Management Society, vol. 29(2), pages 388-409, February.
    7. De Vuyst, Stijn & Bruneel, Herwig & Fiems, Dieter, 2014. "Computationally efficient evaluation of appointment schedules in health care," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1142-1154.
    8. Agrawal, Deepak & Pang, Guodong & Kumara, Soundar, 2023. "Preference based scheduling in a healthcare provider network," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1318-1335.
    9. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    10. Shehadeh, Karmel S. & Cohn, Amy E.M. & Epelman, Marina A., 2019. "Analysis of models for the Stochastic Outpatient Procedure Scheduling Problem," European Journal of Operational Research, Elsevier, vol. 279(3), pages 721-731.
    11. Wen-Ya Wang & Diwakar Gupta, 2011. "Adaptive Appointment Systems with Patient Preferences," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 373-389, July.
    12. Kuiper, Alex & de Mast, Jeroen & Mandjes, Michel, 2021. "The problem of appointment scheduling in outpatient clinics: A multiple case study of clinical practice," Omega, Elsevier, vol. 98(C).
    13. Yu Fu & Amarnath Banerjee, 2021. "A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling," SN Operations Research Forum, Springer, vol. 2(3), pages 1-32, September.
    14. Kuiper, Alex & Mandjes, Michel, 2015. "Appointment scheduling in tandem-type service systems," Omega, Elsevier, vol. 57(PB), pages 145-156.
    15. Karmel S. Shehadeh & Amy E. M. Cohn & Ruiwei Jiang, 2021. "Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 89-111, February.
    16. Lara Wiesche & Matthias Schacht & Brigitte Werners, 2017. "Strategies for interday appointment scheduling in primary care," Health Care Management Science, Springer, vol. 20(3), pages 403-418, September.
    17. Dogru, Ali K. & Melouk, Sharif H., 2019. "Adaptive appointment scheduling for patient-centered medical homes," Omega, Elsevier, vol. 85(C), pages 166-181.
    18. Soltani, Mohamad & Samorani, Michele & Kolfal, Bora, 2019. "Appointment scheduling with multiple providers and stochastic service times," European Journal of Operational Research, Elsevier, vol. 277(2), pages 667-683.
    19. Deceuninck, Matthias & Fiems, Dieter & De Vuyst, Stijn, 2018. "Outpatient scheduling with unpunctual patients and no-shows," European Journal of Operational Research, Elsevier, vol. 265(1), pages 195-207.
    20. 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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