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Comparison and hybridization of crossover operators for the nurse scheduling problem

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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 subject to various hard constraints. In the literature, several genetic algorithms have been proposed 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.

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References

  • Aickelin, U., & Dowsland, K. A. (2000). Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. Journal of Scheduling, 3, 139–153.

    Article  Google Scholar 

  • Aickelin, U., & Dowsland, K. A. (2004). An indirect Genetic Algorithm for a nurse-scheduling problem. Computers and Operations Research, 31, 761–778.

    Article  Google Scholar 

  • Bremermann, H. J. (1962). Optimization through evolution and recombination. In M. C. Yovits, G. T. Jacobi, & G. D. Goldstine (Eds.), Self-organizing systems (pp. 93–106). Washington: Spartan.

    Google Scholar 

  • Burke, E. K., Cowling, P., De Causmacker, P., & Vanden Berghe, G. (2001). A memetic approach to the nurse rostering problem. Applied Intelligence, 3, 199–214.

    Article  Google Scholar 

  • 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, 441–499.

    Article  Google Scholar 

  • Cheang, B., Li, H., Lim, A., & Rodrigues, B. (2003). Nurse rostering problems—a bibliographic survey. European Journal of Operational Research, 151, 447–460.

    Article  Google Scholar 

  • Davis, L. (1985). Applying adaptive algorithms to epistatic domains. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 1, 162–164.

    Google Scholar 

  • Dias, T. M., Ferber, D. F., de Souza, C. C., & Moura, A. V. (2003). Constructing nurse schedules at large hospitals. International Transactions in Operational Research, 10, 245–265.

    Article  Google Scholar 

  • Ernst, A. T., Jiang, H., Krishamoorty, M., Owens, B., & Sier, D. (2004a). Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153, 3–27.

    Article  Google Scholar 

  • Ernst, A. T., Jiang, H., Krishamoorty, M., Owens, B., & Sier, D. (2004b). An annotated bibliography of personnel scheduling and rostering. Annals of Operations Research, 127, 21–144.

    Article  Google Scholar 

  • Felici, G., & Gentile, C. (2004). A polyhedral approach for the staff rostering problem. Management Science, 50, 381–393.

    Article  Google Scholar 

  • Fraser, A. S. (1957). Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Sciences, 10, 484–491.

    Google Scholar 

  • Glover, F., & McMillan, C. (1986). The general employee scheduling problem: an integration of MS and AI. Computers and Operations Research, 13, 563–573.

    Article  Google Scholar 

  • Goldberg, D. E., & Lingle, R. (1985). Alleles, loci, and the travelling salesman problem. In J. Grefenstette (Ed.), Proceedings of the first international conference on genetic algorithms and their applications (pp. 154–159). Hillsdale: Erlbaum.

    Google Scholar 

  • Hansen, P., & Mladenovic, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130, 449–467.

    Article  Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press. Republished by MIT Press (1992).

    Google Scholar 

  • Inoue, T., & Furuhashi, T. (2003). A proposal of combined method of evolutionary algorithm and heuristics for nurse scheduling support system. IEEE Transactions on Industrial Electronics, 50, 833–841.

    Article  Google Scholar 

  • Jan, A., Yamamoto, M., & Ohuchi, A. (2000). Evolutionary algorithms for nurse scheduling problem. In Proceedings of the 2000 congress on evolutionary computation (pp. 196–203).

  • Kapsalis, A., Rayward-Smith, V. J., & Smith, G. D. (1993). Solving the graphical Steiner tree problem using genetic algorithms. Journal of the Operational Research Society, 44, 397–406.

    Article  Google Scholar 

  • Maenhout, B., & Vanhoucke, M. (2006). New computational results for the nurse scheduling problem: a scatter search algorithm. In Lecture notes in computer science (Vol. 3906, pp. 158–170). Berlin: Springer.

    Google Scholar 

  • Maenhout, B., & Vanhoucke, M. (2007). An electromagnetism meta-heuristic for the nurse scheduling problem. Journal of Heuristics, 13, 359–385.

    Article  Google Scholar 

  • Michalewicz, Z. (1995). A survey of constraint handling techniques in evolutionary computation methods. In Proceedings of the fourth annual conference on evolutionary programming (pp. 135–155). San Diego, California.

  • Osogami, T., & Imai, H. (2000). Classification of various neighbourhood operations for the nurse scheduling problem. In Lecture notes in computer science (Vol. 1969, pp. 72–83). Berlin: Springer.

    Google Scholar 

  • Reeves, C. R. (1995). A genetic algorithm for flowshop sequencing. Computers and Operations Research, 22, 5–13.

    Article  Google Scholar 

  • Reeves, C. R. (1996). Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research, 63, 371–396.

    Article  Google Scholar 

  • Syswerda, G. (1996). Schedule optimisation using genetic algorithms. In L. Davis (Ed.), Handbook of genetic algorithms (pp. 335–349). London: Thomson.

    Google Scholar 

  • Thompson, G. M. (1995). Improved implicit optimal modelling of the labour shift scheduling problem. Management Science, 43, 595–607.

    Google Scholar 

  • Vanhoucke, M., & Maenhout, B. (2005). Characterisation and generation of nurse scheduling problem instances (Working Paper 05/339). Ghent University.

  • Vanhoucke, M., & Maenhout, B. (2007). NSPLib—A nurse scheduling problem library: a tool to evaluate (meta-)heuristic procedures. In S. Brailsford & P. Harper (Eds.) Proceedings for the 31st annual meeting of the working group on operations research applied to health services (pp. 151–165). Oxford: Peter Lang.

    Google Scholar 

  • Warner, H. W. (1976). Scheduling nursing personnel according to nursing preference: a mathematical approach. Operations Research, 24, 842–856.

    Article  Google Scholar 

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Correspondence to Mario Vanhoucke.

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Maenhout, B., Vanhoucke, M. Comparison and hybridization of crossover operators for the nurse scheduling problem. Ann Oper Res 159, 333–353 (2008). https://doi.org/10.1007/s10479-007-0268-z

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