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A scatter search methodology for the nurse rostering problem

Author

Listed:
  • E K Burke

    (University of Nottingham)

  • T Curtois

    (University of Nottingham)

  • R Qu

    (University of Nottingham)

  • G Vanden Berghe

    (KaHo St.-Lieven
    K.U.Leuven)

Abstract

The benefits of automating the nurse scheduling process in hospitals include reducing the planning workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and to attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. This paper presents a scatter search approach for the problem of automatically creating nurse rosters. Scatter search is an evolutionary algorithm, which has been successfully applied across a number of problem domains. To adapt and apply scatter search to nurse rostering, it was necessary to develop novel implementations of some of scatter search's subroutines. The algorithm was then tested on publicly available real-world benchmark instances and compared against previously published approaches. The results show the proposed algorithm is a robust and effective method on a wide variety of real-world instances.

Suggested Citation

  • E K Burke & T Curtois & R Qu & G Vanden Berghe, 2010. "A scatter search methodology for the nurse rostering problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1667-1679, November.
  • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:11:d:10.1057_jors.2009.118
    DOI: 10.1057/jors.2009.118
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    References listed on IDEAS

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    1. Beddoe, Gareth R. & Petrovic, Sanja, 2006. "Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering," European Journal of Operational Research, Elsevier, vol. 175(2), pages 649-671, December.
    2. U Aickelin & E K Burke & J Li, 2007. "An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1574-1585, December.
    3. Bellanti, F. & Carello, G. & Della Croce, F. & Tadei, R., 2004. "A greedy-based neighborhood search approach to a nurse rostering problem," European Journal of Operational Research, Elsevier, vol. 153(1), pages 28-40, February.
    4. Jaumard, Brigitte & Semet, Frederic & Vovor, Tsevi, 1998. "A generalized linear programming model for nurse scheduling," European Journal of Operational Research, Elsevier, vol. 107(1), pages 1-18, May.
    5. Burke, Edmund K. & Curtois, Timothy & Post, Gerhard & Qu, Rong & Veltman, Bart, 2008. "A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem," European Journal of Operational Research, Elsevier, vol. 188(2), pages 330-341, July.
    6. G Beddoe & S Petrovic, 2007. "Enhancing case-based reasoning for personnel rostering with selected tabu search concepts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1586-1598, December.
    7. Bard, Jonathan F. & Purnomo, Hadi W., 2005. "Preference scheduling for nurses using column generation," European Journal of Operational Research, Elsevier, vol. 164(2), pages 510-534, July.
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    Cited by:

    1. Burke, Edmund K. & Curtois, Tim, 2014. "New approaches to nurse rostering benchmark instances," European Journal of Operational Research, Elsevier, vol. 237(1), pages 71-81.
    2. Valouxis, Christos & Gogos, Christos & Goulas, George & Alefragis, Panayiotis & Housos, Efthymios, 2012. "A systematic two phase approach for the nurse rostering problem," European Journal of Operational Research, Elsevier, vol. 219(2), pages 425-433.
    3. Lin, Shih-Wei & Ying, Kuo-Ching, 2014. "Minimizing shifts for personnel task scheduling problems: A three-phase algorithm," European Journal of Operational Research, Elsevier, vol. 237(1), pages 323-334.
    4. Federico Della Croce & Fabio Salassa, 2014. "A variable neighborhood search based matheuristic for nurse rostering problems," Annals of Operations Research, Springer, vol. 218(1), pages 185-199, July.
    5. Suk Ho Jin & Ho Yeong Yun & Suk Jae Jeong & Kyung Sup Kim, 2017. "Hybrid and Cooperative Strategies Using Harmony Search and Artificial Immune Systems for Solving the Nurse Rostering Problem," Sustainability, MDPI, vol. 9(7), pages 1-19, June.
    6. Turhan, Aykut Melih & Bilgen, Bilge, 2022. "A mat-heuristic based solution approach for an extended nurse rostering problem with skills and units," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    7. Rajeswari Muniyan & Rajakumar Ramalingam & Sultan S. Alshamrani & Durgaprasad Gangodkar & Ankur Dumka & Rajesh Singh & Anita Gehlot & Mamoon Rashid, 2022. "Artificial Bee Colony Algorithm with Nelder–Mead Method to Solve Nurse Scheduling Problem," Mathematics, MDPI, vol. 10(15), pages 1-24, July.
    8. Chiaramonte Michael & Cochran Jeffery & Caswell David, 2015. "Nurse preference rostering using agents and iterated local search," Annals of Operations Research, Springer, vol. 226(1), pages 443-461, March.
    9. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    10. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "Workforce planning incorporating skills: State of the art," European Journal of Operational Research, Elsevier, vol. 243(1), pages 1-16.

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