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Solving the static INRC-II nurse rostering problem by simulated annealing based on large neighborhoods

Author

Listed:
  • Sara Ceschia

    (University of Udine)

  • Rosita Guido

    (University of Calabria)

  • Andrea Schaerf

    (University of Udine)

Abstract

This paper proposes a local search method based on a large neighborhood to solve the static version of the problem defined for the Second International Nurse Rostering Competition (INRC-II). The search method, driven by a simulated annealing metaheuristic, uses a combination of neighborhoods that either change the assignments of a nurse or swap the assignments of two compatible nurses, for multiple consecutive days. Computational results on the set of competition instances show that our method has been able to improve on all previous approaches on some datasets, and to get close to the best ones in others. Best solutions, along with the datasets and the validation tool, are made available for future comparison.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:288:y:2020:i:1:d:10.1007_s10479-020-03527-6
    DOI: 10.1007/s10479-020-03527-6
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    References listed on IDEAS

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    1. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    2. 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.
    3. 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.
    4. 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.
    5. Florian Mischek & Nysret Musliu, 2019. "Integer programming model extensions for a multi-stage nurse rostering problem," Annals of Operations Research, Springer, vol. 275(1), pages 123-143, April.
    6. 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.
    7. Glass, Celia A. & Knight, Roger A., 2010. "The nurse rostering problem: A critical appraisal of the problem structure," European Journal of Operational Research, Elsevier, vol. 202(2), pages 379-389, April.
    8. Haroldo G. Santos & Túlio A. M. Toffolo & Rafael A. M. Gomes & Sabir Ribas, 2016. "Integer programming techniques for the nurse rostering problem," Annals of Operations Research, Springer, vol. 239(1), pages 225-251, April.
    9. Lü, Zhipeng & Hao, Jin-Kao, 2012. "Adaptive neighborhood search for nurse rostering," European Journal of Operational Research, Elsevier, vol. 218(3), pages 865-876.
    10. Stefaan Haspeslagh & Patrick De Causmaecker & Andrea Schaerf & Martin Stølevik, 2014. "The first international nurse rostering competition 2010," Annals of Operations Research, Springer, vol. 218(1), pages 221-236, July.
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    Cited by:

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    2. Qinyu Zhuo & Weiya Chen & Ziyue Yuan, 2023. "Optimizing Mixed Group Train Operation for Heavy-Haul Railway Transportation: A Case Study in China," Mathematics, MDPI, vol. 11(23), pages 1-16, November.

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