Abstract
In practice nurse rostering problems are often too complex to be expressed through available academic models. Such models are not rich enough to represent the variegated nature of real world scenarios, and therefore have no practical relevance. This article focuses on two particular modelling issues that require careful consideration in making academic nurse rostering approaches re-usable in a real world environment. First: introducing several complex problem characteristics, resulting in a rich, generic model. A detailed description is provided for researchers interested in using this new model. We also present a novel benchmark dataset based on this rich model. Second: the consideration of a consistent evaluation procedure that corresponds to realistic quality measurement. These contributions will enable faster implementation of academic nurse rostering achievements in real hospital environments. A suite of hyper-heuristics is presented. These are capable of solving these rich personnel rostering problems using the presented evaluation procedures. Their performance is compared to that of another meta-heuristic.
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References
Bilgin, B., De Causmaecker, P., Rossie, B., & Vanden Berghe, G. (2010). Local search neighbourhoods for dealing with a novel nurse rostering model. Annals of Operations Research, 194, 33–57.
Brucker, P., Burke, E. K., Curtois, T., Qu, R., & Vanden Berghe, G. (2010). A shift sequence based approach for nurse scheduling and a new benchmark dataset. Journal of Heuristics, 16(4), 559–573.
Burke, E. K., De Causmaecker, P., Petrovic, S., & Vanden Berghe, G. (2001). Fitness evaluation for nurse scheduling problems. In Proceedings of the 2001 congress on evolutionary computation CEC2001 (pp. 1139–1146). New York: IEEE Press.
Burke, E. K., Kendall, G., Newall, J., Hart, E., Ross, P., & Schulenburg, S. (2003). Hyper-heuristics: an emerging direction in modern search technology. In Handbook of metaheuristics (pp. 457–474). New York: Springer, Chap. 16.
Burke, E. K., De Causmaecker, P., Vanden Berghe, G., & Van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7(6), 441–499.
Burke, E. K., De Causmaecker, P., Petrovic, S., & Vanden Berghe, G. (2006). Metaheuristics for handling time interval coverage constraints in nurse scheduling. Applied Artificial Intelligence, 20(9), 743–766.
Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. (2009) A classification of hyper-heuristic approaches. In Handbook of metaheuristics. International series in operations research and management science (pp. 449–468). Berlin: Springer, Chap. 15.
Carrasco, R. C. (2010). Long-term staff scheduling with regular temporal distribution. Computer Methods and Programs in Biomedicine, 100, 191–199.
Curtois, T. (2009). Employee scheduling benchmark dataset. http://www.cs.nott.ac.uk/~tec/NRP/.
De Causmaecker, P., & Vanden Berghe, G. (2011). A categorisation of nurse rostering problems. Journal of Scheduling, 14, 3–16.
De Grano, M. L., Medeiros, D. J., & Eitel, D. (2009). Accommodating individual preferences in nurse scheduling via auctions and optimization. Health Care Management Science, 12, 228–242.
Glass, C. A., & Knight, R. A. (2010). The nurse rostering problem: a critical appraisal of the problem structure. European Journal of Operational Research, 202(2), 379–389.
Haspeslagh, S., De Causmaecker, P., Schaerf, A., & Stolevik, M. (2012). The first international nurse rostering competition 2010. Annals of Operations Research, 1–16.
Isken, M. (2004). An implicit tour scheduling model with applications in healthcare. Annals of Operations Research, 128, 91–109.
Kellogg, D. L., & Walczak, S. (2007). Nurse scheduling: from academia to implementation or not? Interfaces, 37(4), 355–369.
Levner, E., Kats, V., Lopez de Pablo, A., & Cheng, T. C. E. (2010). Complexity of cyclic scheduling problems: a state-of-the-art survey. Computers & Industrial Engineering, 59, 352–361.
Maenhout, B., & Vanhoucke, M. (2008). Comparison and hybridization of crossover operators for the nurse scheduling problem. Annals of Operations Research, 159, 333–353.
Petrovic, S., Beddoe, G., & Vanden Berghe, G. (2003). Storing and adapting repair experiences in personnel rostering. Lecture Notes in Computer Science, 2740, 148–165.
Ronnberg, E., & Larsson, T. (2010). Automating the self-scheduling process of nurses in Swedish healthcare: a pilot study. Health Care Management Science, 13, 35–53.
Vanhoucke, M., & Maenhout, B. (2007) NSPLib—a nurse scheduling problem library: a tool to evaluate (meta-)heuristic procedures. In S. Brailsford & P. Harper (Eds.), Operational research for health policy: making better decisions. Proceedings of the 31st annual meeting of the working group on operations research applied to health services (pp. 151–165).
Wang, Z. G., & Wang, C. (2009). Automating nurse self-rostering: a multiagent systems model. In 2009 IEEE international conference on systems, man and cybernetics, SMC 2009 (vol. 1–9, pp. 4422–4425).
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Part of this research was supported by the Flemish government within the project IWT-080356.
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Smet, P., Bilgin, B., De Causmaecker, P. et al. Modelling and evaluation issues in nurse rostering. Ann Oper Res 218, 303–326 (2014). https://doi.org/10.1007/s10479-012-1116-3
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DOI: https://doi.org/10.1007/s10479-012-1116-3