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A physician planning framework for polyclinics under uncertainty

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  • Tohidi, Mohammad
  • Kazemi Zanjani, Masoumeh
  • Contreras, Ivan

Abstract

In this paper, we present a comprehensive two-level physician planning framework for polyclinics under uncertainty. The first level focuses on clinic scheduling and capacity planning decisions, whereas the second level deals with physician scheduling and operational adjustments decisions. In order to protect the generated schedules against demand uncertainty, the first level is modeled as an adjustable robust scheduling problem, which is solved using an ad hoc cutting plane algorithm. To cope with variability in patients’ treatment times, we formulate the second level as a two-stage stochastic problem and use a sample average approximation scheme to obtain solutions with small optimality gaps. We use a Monte-Carlo simulation algorithm and data obtained from a university health center in Montreal, Canada, to demonstrate the benefits of our planning framework. In particular, we show that the schedule generated by our approach is superior in terms of total cost as compared with the one obtained from a single-level deterministic model.

Suggested Citation

  • Tohidi, Mohammad & Kazemi Zanjani, Masoumeh & Contreras, Ivan, 2021. "A physician planning framework for polyclinics under uncertainty," Omega, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:jomega:v:101:y:2021:i:c:s0305048319308126
    DOI: 10.1016/j.omega.2020.102275
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    Cited by:

    1. 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).

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