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A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling

Author

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  • Yu Fu

    (Texas A&M University)

  • Amarnath Banerjee

    (Texas A&M University)

Abstract

This paper addressed a scheduling problem which handles urgent tasks along with existing schedules. The uncertainties in this problem come from random process of existing schedules and unknown upcoming urgent tasks. To deal with the uncertainties, this paper proposes a stochastic integer programming (SIP) based aggregated online scheduling method. The method is illustrated through a study case from the outpatient clinic block-wise scheduling system which is under a hybrid scheduling policy combining regular far-in-advance policy and the open-access policy. The COVID-19 pandemic brings more challenges for the healthcare system including the fluctuations of service time and increasing urgent requests which this paper is designed for. The schedule framework designed in the method is comprehensive to accommodate various uncertainties in the healthcare service system, such as: no-shows, cancellations and punctuality of patients as well as preference of patients over time slots and physicians.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:snopef:v:2:y:2021:i:3:d:10.1007_s43069-021-00089-6
    DOI: 10.1007/s43069-021-00089-6
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    References listed on IDEAS

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    Cited by:

    1. Nathan Preuss & Lin Guo & Janet K. Allen & Farrokh Mistree, 2022. "Improving Patient Flow in a Primary Care Clinic," SN Operations Research Forum, Springer, vol. 3(3), pages 1-22, September.

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