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A Comparison and Hybridization of Crossover Operators for the Nurse Scheduling Problem

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  • B. MAENHOUT
  • M. VANHOUCKE

Abstract

In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NSP). The NSP involves the construction of roster schedules for nursing staff in order to maximize the quality of the roster schedule and to minimize the violations of the minimal coverage requirements subject to various hard case-specific constraints. In literature, several genetic algorithms have been proposed in literature to solve the NSP under various assumptions. The contribution of this paper is twofold. First, we extensively compare the various crossover operators and test them on a standard dataset in a solitary approach. Second, we propose several options to hybridize the various crossover operators.

Suggested Citation

  • B. Maenhout & M. Vanhoucke, 2006. "A Comparison and Hybridization of Crossover Operators for the Nurse Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/366, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:06/366
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    1. A.T. Ernst & H. Jiang & M. Krishnamoorthy & B. Owens & D. Sier, 2004. "An Annotated Bibliography of Personnel Scheduling and Rostering," Annals of Operations Research, Springer, vol. 127(1), pages 21-144, March.
    2. M. Vanhoucke & B. Maenhout, 2005. "Characterisation and Generation of Nurse Scheduling Problem Instances," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/339, Ghent University, Faculty of Economics and Business Administration.
    3. B. Maenhout & M. Vanhoucke, 2005. "New Computational Results for the Nurse Scheduling Problem: A Scatter Search Algorithm," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/341, Ghent University, Faculty of Economics and Business Administration.
    4. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    5. Giovanni Felici & Claudio Gentile, 2004. "A Polyhedral Approach for the Staff Rostering Problem," Management Science, INFORMS, vol. 50(3), pages 381-393, March.
    6. Cheang, B. & Li, H. & Lim, A. & Rodrigues, B., 2003. "Nurse rostering problems--a bibliographic survey," European Journal of Operational Research, Elsevier, vol. 151(3), pages 447-460, December.
    7. D. Michael Warner, 1976. "Scheduling Nursing Personnel According to Nursing Preference: A Mathematical Programming Approach," Operations Research, INFORMS, vol. 24(5), pages 842-856, October.
    8. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    9. Gary M. Thompson, 1995. "Improved Implicit Optimal Modeling of the Labor Shift Scheduling Problem," Management Science, INFORMS, vol. 41(4), pages 595-607, April.
    10. B. Maenhout & M. Vanhoucke, 2005. "An Electromagnetic Meta-Heuristic for the Nurse Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/316, Ghent University, Faculty of Economics and Business Administration.
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    Cited by:

    1. 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.
    2. Burak Bilgin & Patrick Causmaecker & Benoît Rossie & Greet Vanden Berghe, 2012. "Local search neighbourhoods for dealing with a novel nurse rostering model," Annals of Operations Research, Springer, vol. 194(1), pages 33-57, April.
    3. Ademir Constantino & Dario Landa-Silva & Everton Melo & Candido Mendonça & Douglas Rizzato & Wesley Romão, 2014. "A heuristic algorithm based on multi-assignment procedures for nurse scheduling," Annals of Operations Research, Springer, vol. 218(1), pages 165-183, July.
    4. Pieter Smet & Burak Bilgin & Patrick De Causmaecker & Greet Vanden Berghe, 2014. "Modelling and evaluation issues in nurse rostering," Annals of Operations Research, Springer, vol. 218(1), pages 303-326, July.
    5. Vanhoucke, Mario & Maenhout, Broos, 2009. "On the characterization and generation of nurse scheduling problem instances," European Journal of Operational Research, Elsevier, vol. 196(2), pages 457-467, July.
    6. Li, Gang & Jiang, Hongxun & He, Tian, 2015. "A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem," Omega, Elsevier, vol. 50(C), pages 1-17.
    7. Peyman Kiani Nahand & Mahdi Hamid & Mahdi Bastan & Ali Mollajan, 2019. "Human resource management: new approach to nurse scheduling by considering human error," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1429-1443, December.
    8. B Maenhout & M Vanhoucke, 2009. "The impact of incorporating nurse-specific characteristics in a cyclical scheduling approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1683-1698, December.
    9. Tom Rihm & Philipp Baumann, 2018. "Staff assignment with lexicographically ordered acceptance levels," Journal of Scheduling, Springer, vol. 21(2), pages 167-189, April.

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    Keywords

    meta-heuristics; hybridization; nurse scheduling;
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