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A distributionally robust optimization approach for outpatient colonoscopy scheduling

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  • Shehadeh, Karmel S.
  • Cohn, Amy E.M.
  • Jiang, Ruiwei

Abstract

We consider the outpatient colonoscopy scheduling problem, recognizing the impact of pre-procedure bowel preparation (prep) quality on the variability in colonoscopy duration. Data from a large academic medical center indicates that colonoscopy durations are bimodal, i.e., depending on the prep quality they can follow two different probability distributions, one for those with adequate prep and the other for those with inadequate prep. We therefore define a distributionally robust outpatient colonoscopy scheduling (DROCS) problem that seeks optimal appointment sequence and schedule to minimize the worst-case weighted expected sum of patient waiting, provider idling, and provider overtime, where the worst-case is taken over an ambiguity set (a family of distributions) characterized through the known mean and support of the prep quality and durations. We derive an equivalent mixed-integer linear programming formulation to solve DROCS. Finally, we present a case study based on extensive numerical experiments in which we draw several managerial insights into colonoscopy scheduling.

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  • 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.
    • Handle: RePEc:eee:ejores:v:283:y:2020:i:2:p:549-561
    DOI: 10.1016/j.ejor.2019.11.039
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    References listed on IDEAS

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    Cited by:

    1. J. Behnamian & Z. Gharabaghli, 2023. "Multi-objective outpatient scheduling in health centers considering resource constraints and service quality: a robust optimization approach," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-35, March.
    2. Shehadeh, Karmel S. & Padman, Rema, 2021. "A distributionally robust optimization approach for stochastic elective surgery scheduling with limited intensive care unit capacity," European Journal of Operational Research, Elsevier, vol. 290(3), pages 901-913.
    3. Novak, Antonin & Gnatowski, Andrzej & Sucha, Premysl, 2022. "Distributionally robust scheduling algorithms for total flow time minimization on parallel machines using norm regularizations," European Journal of Operational Research, Elsevier, vol. 302(2), pages 438-455.
    4. Tsang, Man Yiu & Shehadeh, Karmel S., 2023. "Stochastic optimization models for a home service routing and appointment scheduling problem with random travel and service times," European Journal of Operational Research, Elsevier, vol. 307(1), pages 48-63.
    5. Novak, Antonin & Sucha, Premysl & Novotny, Matej & Stec, Richard & Hanzalek, Zdenek, 2022. "Scheduling jobs with normally distributed processing times on parallel machines," European Journal of Operational Research, Elsevier, vol. 297(2), pages 422-441.
    6. Haimin Lu & Zhi Pei, 2024. "A distributionally robust approach for the two-machine permutation flow shop scheduling," Annals of Operations Research, Springer, vol. 338(1), pages 709-739, July.
    7. Lu, Haimin & Pei, Zhi, 2023. "Single machine scheduling with release dates: A distributionally robust approach," European Journal of Operational Research, Elsevier, vol. 308(1), pages 19-37.
    8. 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.

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