Abstract
At the beginning of the pandemic last year some hospitals had to change their physician schedules to take into account infection risks and potential quarantines for personnel. This was especially important for hospitals that care for high-risk patients, like the St. Anna Children’s Hospital in Vienna, which is a tertiary care center for pediatric oncology. It was very important to develop solving methods for this complex problem in short time. We relied on constraint solving technology which proved to be very useful in such critical situations. In this paper we present a constraint model that includes the variety of requirements that are needed to ensure day-to-day operations as well as the additional constraints imposed by the pandemic situation. We introduce an innovative set of grouping constraints to partition the staff, with the intention to easily isolate a small group in case of an infection. The produced schedules also keep part of the staff as backup to replace personnel in quarantine. In our case study, we evaluate and compare our proposed model on several state-of-the-art solvers. Our approach could successfully produce a high-quality schedule for the considered real-world planning scenario, also compared to solutions found by human planners with considerable effort.
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We make use of the Iverson brackets: \([P] = 1\), if \(P = true\) and \([P] = 0\) if \(P = false\).
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Weeks are assumed to start on the first day of the schedule.
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These weights were determined by the hospital staff.
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The anonymized instances are available at https://cdlab-artis.dbai.tuwien.ac.at/papers/pandemic-scheduling/.
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Acknowledgments
The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged.
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Geibinger, T., Kletzander, L., Krainz, M., Mischek, F., Musliu, N., Winter, F. (2021). Physician Scheduling During a Pandemic. In: Stuckey, P.J. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2021. Lecture Notes in Computer Science(), vol 12735. Springer, Cham. https://doi.org/10.1007/978-3-030-78230-6_29
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DOI: https://doi.org/10.1007/978-3-030-78230-6_29
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