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Determination of Optimal Variable-Sized Multiple-Block Appointment Systems

Author

Listed:
  • Brant E. Fries

    (Yale School of Organization and Management, New Haven, Connecticut)

  • Vijay P. Marathe

    (American Telephone and Telegraph, Basking Ridge, New Jersey)

Abstract

The single-block appointment system is the most common method of scheduling ambulatory care clinics today. Several studies have examined various appointment systems ranging from single-block appointments on one extreme to individual appointments on the other, and including mixtures of these such as multiple-block ( m -at-a-time) and block/individual systems. In this paper we analyze a general single-server multiple-block system, one permitting blocks of variable size. In the analysis we use a dynamic programming approach, with some modifications to compensate for the non-Markov nature of the problem. Analytical results and approximations which significantly reduce the computational requirements for a solution are obtained. Examples demonstrate that under certain weightings of the criteria of waiting, idle, and overtime, the generality of the system considered here allows performance superior to that of other commonly used systems.

Suggested Citation

  • Brant E. Fries & Vijay P. Marathe, 1981. "Determination of Optimal Variable-Sized Multiple-Block Appointment Systems," Operations Research, INFORMS, vol. 29(2), pages 324-345, April.
    • Handle: RePEc:inm:oropre:v:29:y:1981:i:2:p:324-345
    DOI: 10.1287/opre.29.2.324
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    Cited by:

    1. Kemper, Benjamin & Klaassen, Chris A.J. & Mandjes, Michel, 2014. "Optimized appointment scheduling," European Journal of Operational Research, Elsevier, vol. 239(1), pages 243-255.
    2. Sabine Sickinger & Rainer Kolisch, 2009. "The performance of a generalized Bailey–Welch rule for outpatient appointment scheduling under inpatient and emergency demand," Health Care Management Science, Springer, vol. 12(4), pages 408-419, December.
    3. Paola Cappanera & Filippo Visintin & Carlo Banditori & Daniele Feo, 2019. "Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 212-254, March.
    4. Vink, Wouter & Kuiper, Alex & Kemper, Benjamin & Bhulai, Sandjai, 2015. "Optimal appointment scheduling in continuous time: The lag order approximation method," European Journal of Operational Research, Elsevier, vol. 240(1), pages 213-219.
    5. 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).
    6. Tugba Cayirli & Kum Khiong Yang & Ser Aik Quek, 2012. "A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins," Production and Operations Management, Production and Operations Management Society, vol. 21(4), pages 682-697, July.
    7. Qu, Xiuli & Rardin, Ronald L. & Williams, Julie Ann S. & Willis, Deanna R., 2007. "Matching daily healthcare provider capacity to demand in advanced access scheduling systems," European Journal of Operational Research, Elsevier, vol. 183(2), pages 812-826, December.
    8. Qu, Xiuli & Shi, Jing, 2011. "Modeling the effect of patient choice on the performance of open access scheduling," International Journal of Production Economics, Elsevier, vol. 129(2), pages 314-327, February.
    9. 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.
    10. Yasin Gocgun & Martin Puterman, 2014. "Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking," Health Care Management Science, Springer, vol. 17(1), pages 60-76, March.
    11. Marie Matta & Sarah Patterson, 2007. "Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center," Health Care Management Science, Springer, vol. 10(2), pages 173-194, June.
    12. Lawrence W. Robinson & Rachel R. Chen, 2011. "Estimating the Implied Value of the Customer's Waiting Time," Manufacturing & Service Operations Management, INFORMS, vol. 13(1), pages 53-57, February.
    13. Thu-Ba T. Nguyen & Appa Iyer Sivakumar & Stephen C. Graves, 2017. "Scheduling rules to achieve lead-time targets in outpatient appointment systems," Health Care Management Science, Springer, vol. 20(4), pages 578-589, December.
    14. Chi-Lun Rau & Pei-Fang Tsai & Sheau-Farn Liang & Jhih-Cian Tan & Hong-Cheng Syu & Yue-Ling Jheng & Ting-Syuan Ciou & Fu-Shan Jaw, 2013. "Using discrete-event simulation in strategic capacity planning for an outpatient physical therapy service," Health Care Management Science, Springer, vol. 16(4), pages 352-365, December.
    15. Tsai, Pei-Fang Jennifer & Teng, Guei-Yu, 2014. "A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic," European Journal of Operational Research, Elsevier, vol. 239(2), pages 427-436.
    16. Linda V. Green & Sergei Savin & Ben Wang, 2006. "Managing Patient Service in a Diagnostic Medical Facility," Operations Research, INFORMS, vol. 54(1), pages 11-25, February.
    17. Jianzhe Luo & Vidyadhar G. Kulkarni & Serhan Ziya, 2012. "Appointment Scheduling Under Patient No-Shows and Service Interruptions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 670-684, October.
    18. 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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