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Central intake optimization and decentralized decomposition for appointment scheduling and sequencing

Author

Listed:
  • Pardis Seyedi

    (Sharif University of Technology
    University of Toronto)

  • Michael W. Carter

    (University of Toronto)

  • Kourosh Eshghi

    (Sharif University of Technology)

Abstract

An efficient appointment scheduling system has a defining role in controlling wait times and improving the productivity of a large variety of service systems. This study addresses the variability and length of wait times. We reduce them by a form of restricted central intake. We believe it is the first study that expands the appointment scheduling-sequencing model to include multiple sites and incorporate clients’ flexibility and priorities, and solves the large-size scheduling-sequencing problem in a decentralized manner. To make the study more practical, it should be compatible with the multi-stakeholder environment and consider their independency. Furthermore, the problem is large-size as it combines all requests from a geographical region into one stream. Therefore, a decentralized distributed algorithm is applied to solve the amended model. The solution approach is an ADMM-based combination of dual decomposition and augmented Lagrangian relaxation. For the application of this approach, this paper focuses on the outpatient appointment system due to its importance. Early diagnosis and prevention play a crucial role in community health and health system quality. However, patients often experience significant wait times for various diagnostic technologies worldwide. The approach is examined by a real situation of MRI in Ontario, Canada. It has been shown that this study provides better workload balance across hospitals, better responding to demand fluctuations, and alleviates excessive wait times. The computational results also show that the proposed solution method can be satisfactory in terms of accuracy, running time, and applicability. The approach developed in this study can be applicable to many practical applications of timing and sequencing, such as outpatient surgery, other diagnostic testing, home healthcare, and physical and mental therapies, as well as in other service industries beyond healthcare, like public consultations, government services.

Suggested Citation

  • Pardis Seyedi & Michael W. Carter & Kourosh Eshghi, 2025. "Central intake optimization and decentralized decomposition for appointment scheduling and sequencing," Flexible Services and Manufacturing Journal, Springer, vol. 37(1), pages 208-253, March.
  • Handle: RePEc:spr:flsman:v:37:y:2025:i:1:d:10.1007_s10696-024-09538-w
    DOI: 10.1007/s10696-024-09538-w
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    References listed on IDEAS

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    1. 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.
    2. S. Ayca Erdogan & Alexander Gose & Brian T. Denton, 2015. "Online appointment sequencing and scheduling," IISE Transactions, Taylor & Francis Journals, vol. 47(11), pages 1267-1286, November.
    3. Bruno Vieira & Derya Demirtas & Jeroen B. Kamer & Erwin W. Hans & Louis-Martin Rousseau & Nadia Lahrichi & Wim H. Harten, 2020. "Radiotherapy treatment scheduling considering time window preferences," Health Care Management Science, Springer, vol. 23(4), pages 520-534, December.
    4. Y Huang & P Zuniga, 2012. "Dynamic overbooking scheduling system to improve patient access," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(6), pages 810-820, June.
    5. Ho-Yin Mak & Ying Rong & Jiawei Zhang, 2015. "Appointment Scheduling with Limited Distributional Information," Management Science, INFORMS, vol. 61(2), pages 316-334, February.
    6. C. Y. Wang & X. Q. Yang & X. M. Yang, 2013. "Nonlinear Augmented Lagrangian and Duality Theory," Mathematics of Operations Research, INFORMS, vol. 38(4), pages 740-760, November.
    7. Deren Han & Xiaoming Yuan, 2012. "A Note on the Alternating Direction Method of Multipliers," Journal of Optimization Theory and Applications, Springer, vol. 155(1), pages 227-238, October.
    8. Camilo Mancilla & Robert Storer, 2012. "A sample average approximation approach to stochastic appointment sequencing and scheduling," IISE Transactions, Taylor & Francis Journals, vol. 44(8), pages 655-670.
    9. Wu, Xueqi & Zhou, Shenghai, 2022. "Sequencing and scheduling appointments on multiple servers with stochastic service durations and customer arrivals," Omega, Elsevier, vol. 106(C).
    10. Nur Banu Demir & Serhat Gul & Melih Çelik, 2021. "A stochastic programming approach for chemotherapy appointment scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 112-133, February.
    11. 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.
    12. Shehadeh, Karmel S. & Cohn, Amy E.M. & Jiang, Ruiwei, 2020. "A distributionally robust optimization approach for outpatient colonoscopy scheduling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 549-561.
    13. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    14. Seung Jun Lee & Gregory R. Heim & Chelliah Sriskandarajah & Yunxia Zhu, 2018. "Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No†Shows," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 28-48, January.
    15. Huaxin Qiu & Dujuan Wang & Yanzhang Wang & Yunqiang Yin, 2019. "MRI appointment scheduling with uncertain examination time," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 62-82, January.
    16. Yong-Hong Kuo & Hari Balasubramanian & Yan Chen, 2020. "Medical appointment overbooking and optimal scheduling: tradeoffs between schedule efficiency and accessibility to service," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 72-101, March.
    17. Yichuan Ding & Eric Park & Mahesh Nagarajan & Eric Grafstein, 2019. "Patient Prioritization in Emergency Department Triage Systems: An Empirical Study of the Canadian Triage and Acuity Scale (CTAS)," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 723-741, October.
    18. Rachel R. Chen & Lawrence W. Robinson, 2014. "Sequencing and Scheduling Appointments with Potential Call-In Patients," Production and Operations Management, Production and Operations Management Society, vol. 23(9), pages 1522-1538, September.
    19. Bo Zeng & Ayten Turkcan & Ji Lin & Mark Lawley, 2010. "Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities," Annals of Operations Research, Springer, vol. 178(1), pages 121-144, July.
    20. 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.
    21. Yao, Yu & Zhu, Xiaoning & Dong, Hongyu & Wu, Shengnan & Wu, Hailong & Carol Tong, Lu & Zhou, Xuesong, 2019. "ADMM-based problem decomposition scheme for vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 156-174.
    22. 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.
    23. Pan, Xingwei & Geng, Na & Xie, Xiaolan, 2021. "Appointment scheduling and real-time sequencing strategies for patient unpunctuality," European Journal of Operational Research, Elsevier, vol. 295(1), pages 246-260.
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