Abstract
Our specific problem is to create daily schedules of elective surgeries in a multiple operating room setting with the goals of minimizing the amount of overtime incurred and maintaining patient volumes. While surgical durations cannot always be perfectly estimated and vary by procedure and surgeon, our approach relies on leveraging the stochastic nature of surgical durations to simulate each operating day and understand the probability of incurring overtime under a certain schedule of surgeries. The heuristic optimization component of our approach investigates the probabilistic evaluation and strategically re-schedules surgeries. Through experimentation with three optimization techniques, two showed promising results being able to reduce the total number of overtime surgeries by 12–15%, equivalent to approximately 1h of total monthly overtime. Compared to the literature, this approach serves solely as a tool for improving schedules and can be used for supporting decision making at the hospital. Our contribution involves introducing the simulation optimization model and describing the data-driven approach to analyzing the scheduling problem.
Stephen Chen: Supported by Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discovery Grant – RGPIN-2022-04524.
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Maltseva, D., Chen, S., Lex, J., Abbas, A., Whyne, C. (2024). Simulation Optimization of Operating Room Schedules for Elective Surgeries. In: Fujita, H., Cimler, R., Hernandez-Matamoros, A., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2024. Lecture Notes in Computer Science(), vol 14748. Springer, Singapore. https://doi.org/10.1007/978-981-97-4677-4_31
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