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Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine

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Learning and Intelligent Optimization (LION 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11968))

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Abstract

The problem of generating surgery schedules is formulated as a mathematical model with probabilistic constraints. The approach presented is a new method for tackling probabilistic constraints using machine learning. The technique is inspired by models that use slacks in capacity planning. Essentially support vector classification is used to learn a linear constraint that will replace the probabilistic constraint. The data used to learn this constraint is labeled using Monte Carlo simulations. This data is iteratively discovered, during the optimization procedure, and augmented to the training set. The linear support vector classifier is then updated during search, until a feasible solution is discovered. The stochastic surgery model presented is inspired by real challenges faced by many hospitals today and tested on real-life data.

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Acknowledgement

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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Correspondence to Thomas Philip Runarsson .

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Runarsson, T.P. (2020). Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_10

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