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1University of Georgia; 2Stanford University; 3University of Waterloo
Strategies for scheduling patients to minimize resource and waiting costs is a key problem in healthcare operation. The provider should decide patients to schedule as well as future appointment times to assign. We consider the multi-class advance scheduling problem with random arrivals. We propose and analyze the optimal policy that determines patients and appointments for the current state, effectively prioritizes the patients, and efficiently balances the workload across the booking window.
Predictive prescriptions for surgery scheduling
Dominik David Walzner1, Andreas Fügener1, Christof Denz2
1Department of Supply Chain Management, University of Cologne, Germany; 2University Hospital Cologne, Germany
We propose a data-driven method for surgery scheduling that combines AI algorithms with stochastic optimization techniques. While existing approaches only consider the surgery type to differentiate between surgeries, our method allows us to consider different surgery-, patient- and physician-related aspects. We find that our method outperforms an existing method which only takes procedure types into account, resulting in higher operating room utilization and lower waiting times.
Dynamic inter-day and intra-day scheduling
Christos Zacharias1, Nan Liu2, Mehmet A. Begen3
1University of Miami Herbert Business School; 2Boston College Carroll School of Management; 3Western University Ivey Business School
The simultaneous consideration of dynamic inter-day and intra-day scheduling decisions is an established theoretical and practical problem that has remained open due to its highly stochastic nature, complex structure, and the curse of dimensionality. We develop the first analytical optimization model and theoretical results addressing this joint problem within a computationally tractable optimization framework with theoretical performance guarantees.