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A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems

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  • Burke, Edmund K.
  • Li, Jingpeng
  • Qu, Rong

Abstract

This paper presents a hybrid multi-objective model that combines integer programming (IP) and variable neighbourhood search (VNS) to deal with highly-constrained nurse rostering problems in modern hospital environments. An IP is first used to solve the subproblem which includes the full set of hard constraints and a subset of soft constrains. A basic VNS then follows as a postprocessing procedure to further improve the IP's resulting solutions. The satisfaction of the excluded constraints from the preceding IP model is the major focus of the VNS. Very promising results are reported compared with a commercial genetic algorithm and a hybrid VNS approach on real instances arising in a Dutch hospital. The comparison results demonstrate that our hybrid approach combines the advantages of both the IP and the VNS to beat other approaches in solving this type of problems. We also believe that the proposed methodology can be applied to other resource allocation problems with a large number of constraints.

Suggested Citation

  • Burke, Edmund K. & Li, Jingpeng & Qu, Rong, 2010. "A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems," European Journal of Operational Research, Elsevier, vol. 203(2), pages 484-493, June.
  • Handle: RePEc:eee:ejores:v:203:y:2010:i:2:p:484-493
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    References listed on IDEAS

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    1. Beaumont, Nicholas, 1997. "Scheduling staff using mixed integer programming," European Journal of Operational Research, Elsevier, vol. 98(3), pages 473-484, May.
    2. D. Michael Warner & Juan Prawda, 1972. "A Mathematical Programming Model for Scheduling Nursing Personnel in a Hospital," Management Science, INFORMS, vol. 19(4-Part-1), pages 411-422, December.
    3. Easton, Fred F. & Mansour, Nashat, 1999. "A distributed genetic algorithm for deterministic and stochastic labor scheduling problems," European Journal of Operational Research, Elsevier, vol. 118(3), pages 505-523, November.
    4. Berrada, Ilham & Ferland, Jacques A. & Michelon, Philippe, 1996. "A multi-objective approach to nurse scheduling with both hard and soft constraints," Socio-Economic Planning Sciences, Elsevier, vol. 30(3), pages 183-193, September.
    5. 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.
    6. 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.
    7. Uwe Aickelin & Jingpeng Li, 2007. "An estimation of distribution algorithm for nurse scheduling," Annals of Operations Research, Springer, vol. 155(1), pages 289-309, November.
    8. 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.
    9. 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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