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2020 | OriginalPaper | Buchkapitel

Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine

verfasst von : Thomas Philip Runarsson

Erschienen in: Learning and Intelligent Optimization

Verlag: Springer International Publishing

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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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Titel: Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine
verfasst von: Thomas Philip Runarsson
Verlag: Springer International Publishing
Buch: Learning and Intelligent Optimization
Print ISBN: 978-3-030-38628-3

Electronic ISBN: 978-3-030-38629-0

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-38629-0_10

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