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Balancing Nursing Workload by Constraint Programming

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Integration of AI and OR Techniques in Constraint Programming (CPAIOR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9676))

Abstract

Nursing workload in hospitals has an impact on the quality of care and on job satisfaction. Understandably there has been much recent research on improving the staffing and nurse-patient assignment decisions in increasingly realistic settings. On a version of the nurse-patient assignment problem given a fixed staffing of neonatal intensive care units, constraint programming (CP) was shown to perform better than competing optimization methods. In this paper we take advantage of recent improvements to the CP approach to solve the integrated problem of staffing and nurse-patient assignment. We then consider a more difficult but also more realistic version of the problem in which patients are categorized into a small number of types and the workload associated with each type is nurse-dependent.

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Notes

  1. 1.

    Personal communication from the authors of [2].

  2. 2.

    For these 2-zone instances they can show that their solutions are optimal for the integrated problem. Their initial model combining staffing and nurse-patient assignment took about two orders of magnitude more time.

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Acknowledgements

Financial support for this research was provided by Discovery Grant 218028/2012 from the Natural Sciences and Engineering Research Council of Canada.

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Authors and Affiliations

  1. École Polytechnique de Montréal, Montreal, Canada

    Gilles Pesant

  2. CIRRELT, Université de Montréal, Montreal, Canada

    Gilles Pesant

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  1. Gilles Pesant
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Correspondence to Gilles Pesant .

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Editors and Affiliations

  1. Université Laval, Quebec, Québec, Canada

    Claude-Guy Quimper

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Pesant, G. (2016). Balancing Nursing Workload by Constraint Programming. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-33954-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33953-5

  • Online ISBN: 978-3-319-33954-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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