Abstract
The problem of generating surgery schedules is formulated as a mathematical model with probabilistic constraints. The approach presented uses modern machine learning to approximate the model’s probabilistic constraints. The technique is inspired by models that use slacks in capacity planning. Essentially a neural-network is used to learn linear constraints that will replace the probabilistic constraint. The data used to learn these constraints is verified and labeled using Monte Carlo simulations. The solutions iteratively discovered, during the optimization procedure, produce also new training data. The neural-network continues its training on this data until the solution discovered is verified to be feasible. The stochastic surgery model studied is inspired by real challenges faced by many hospitals today and tested on real-life data.
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The author would like to acknowledge the National University Hospital of Iceland for providing data, insights and support for this project.
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Runarsson, T.P. (2019). Approximating Probabilistic Constraints for Surgery Scheduling Using Neural Networks. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_53
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DOI: https://doi.org/10.1007/978-3-030-37599-7_53
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