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Adaptive neighborhood search for nurse rostering

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  • Lü, Zhipeng
  • Hao, Jin-Kao

Abstract

This paper presents an adaptive neighborhood search method (ANS) for solving the nurse rostering problem proposed for the First International Nurse Rostering Competition (INRC-2010). ANS uses jointly two distinct neighborhood moves and adaptively switches among three intensification and diversification search strategies according to the search history. Computational results assessed on the three sets of 60 competition instances show that ANS improves the best known results for 12 instances while matching the best bounds for 39 other instances. An analysis of some key elements of ANS sheds light on the understanding of the behavior of the proposed algorithm.

Suggested Citation

  • Lü, Zhipeng & Hao, Jin-Kao, 2012. "Adaptive neighborhood search for nurse rostering," European Journal of Operational Research, Elsevier, vol. 218(3), pages 865-876.
    • Handle: RePEc:eee:ejores:v:218:y:2012:i:3:p:865-876
    DOI: 10.1016/j.ejor.2011.12.016
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    References listed on IDEAS

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    9. Lü, Zhipeng & Hao, Jin-Kao, 2010. "Adaptive Tabu Search for course timetabling," European Journal of Operational Research, Elsevier, vol. 200(1), pages 235-244, January.
    10. 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.
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    Cited by:

    1. Zeng, Zhizhong & Yu, Xinguo & He, Kun & Huang, Wenqi & Fu, Zhanghua, 2016. "Iterated Tabu Search and Variable Neighborhood Descent for packing unequal circles into a circular container," European Journal of Operational Research, Elsevier, vol. 250(2), pages 615-627.
    2. Frederik Knust & Lin Xie, 2019. "Simulated annealing approach to nurse rostering benchmark and real-world instances," Annals of Operations Research, Springer, vol. 272(1), pages 187-216, January.
    3. 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.
    4. Mehleri, E.D. & Sarimveis, H. & Markatos, N.C. & Papageorgiou, L.G., 2013. "Optimal design and operation of distributed energy systems: Application to Greek residential sector," Renewable Energy, Elsevier, vol. 51(C), pages 331-342.
    5. Sara Ceschia & Nguyen Dang & Patrick Causmaecker & Stefaan Haspeslagh & Andrea Schaerf, 2019. "The Second International Nurse Rostering Competition," Annals of Operations Research, Springer, vol. 274(1), pages 171-186, March.
    6. Rahimian, Erfan & Akartunalı, Kerem & Levine, John, 2017. "A hybrid Integer Programming and Variable Neighbourhood Search algorithm to solve Nurse Rostering Problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 411-423.
    7. 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.
    8. Meignan, David & Knust, Sigrid, 2019. "A neutrality-based iterated local search for shift scheduling optimization and interactive reoptimization," European Journal of Operational Research, Elsevier, vol. 279(2), pages 320-334.
    9. Sara Ceschia & Rosita Guido & Andrea Schaerf, 2020. "Solving the static INRC-II nurse rostering problem by simulated annealing based on large neighborhoods," Annals of Operations Research, Springer, vol. 288(1), pages 95-113, May.

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