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Machine Learning: Multi-site Evidence-Based Best Practice Discovery Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Dynamic Programming with Approximation Function for Nurse Scheduling

Authors : Peng Shi, Dario Landa-Silva

Published in: Machine Learning, Optimization, and Big Data

Publisher: Springer International Publishing

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Abstract

Although dynamic programming could ideally solve any combinatorial optimization problem, the curse of dimensionality of the search space seriously limits its application to large optimization problems. For example, only few papers in the literature have reported the application of dynamic programming to workforce scheduling problems. This paper investigates approximate dynamic programming to tackle nurse scheduling problems of size that dynamic programming cannot tackle in practice. Nurse scheduling is one of the problems within workforce scheduling that has been tackled with a considerable number of algorithms particularly meta-heuristics. Experimental results indicate that approximate dynamic programming is a suitable method to solve this problem effectively.

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Metadata
Title
Dynamic Programming with Approximation Function for Nurse Scheduling
Authors
Peng Shi
Dario Landa-Silva
Copyright Year
2016
Book
Machine Learning, Optimization, and Big Data

Print ISBN: 978-3-319-51468-0

Electronic ISBN: 978-3-319-51469-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-51469-7_23

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