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IMA Journal of Management Mathematics Advance Access originally published online on November 20, 2008
IMA Journal of Management Mathematics 2009 20(4):369-383; doi:10.1093/imaman/dpn033
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© The authors 2008. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

This article appears in the following IMA Journal of Management Mathematics issue: Special Issue Applying Mathematics to Problems in Health Care [View the issue table of contents]

A genetic algorithm approach to the nurse scheduling problem with fuzzy preferences

Alejandra Duenas{dagger}

Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK

G. Yazgi Tütüncü{ddagger}

IESEG, School of Management, CNRS, LEM, UMR 8179, 3 Rue Digue, F-59000, Lille, France

James B. Chilcott§

Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK

{dagger} Corresponding author. Email: a.duenas{at}sheffield.ac.uk

{ddagger} Email: yazgi.tutuncu{at}yahoo.com

§ Email: j.b.chilcott{at}sheffield.ac.uk

Received on 1 May 2007. Accepted on 1 May 2008.

The nurse scheduling problem (NSP) consists of generating a work schedule for nursing staff in a hospital. The approach presented in this paper considers a multi-objective NSP involving the nurses’ preferences. These preferences are modelled by fuzzy sets and aggregated to determine an overall preference cost function. The schedules are generated by a hybrid approach based on an interactive sequential multi-objective problem solving method combined with a genetic algorithm (GA). The head nurse is identified as the decision maker. Different versions of the GA are developed to test the efficiency of the approach. The results reveal that the proposed approach generates good quality solutions and due to its flexibility may be applicable in real-life NSPs.

Keywords: nurse scheduling problem; multi-objective problems; genetic algorithm; fuzzy sets


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