Abstract
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence build better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.
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References
Aickelin, U. (2002). "An Indirect Genetic Algorithm for Set Covering Problems." Journal of the Operational Research Society 53(10), 1118–1126.
Aickelin, U. and K. Dowsland. (2000). "Exploiting Problem Structure in a Genetic Algorithms Approach to a Nurse Rostering Problem." Journal of Scheduling 31, 139–153.
Aickelin, U. and K. Dowsland. (2002). "A Comparison of Indirect Genetic Algorithm Approaches to Multiple Choice Problems." Journal of Heuristics 8(5), 503–514.
Bäck, T. (1993). Applications of Evolutionary Algorithms, 5th edn. Dortmund, Germany.
Bradley, D. and J. Martin. (1990). "Continuous Personnel Scheduling Algorithms: A Literature Review." Journal of the Society for Health Systems 2, 8–23.
Chaiyaratana, N. and A. Zalzala. (1997). "Recent Developments in Evolutionary and Genetic Algorithms: Theory and Applications." In P. Fleming and S. Zalzala (eds.), Genetic Algorithms in Engineering Systems, Vol. 2: Innovations and Applications, Letchworth: Omega Print & Design, IEEE, pp. 270–277.
Conover, W.J. (1980). Practical Nonparametric Statistics, 2nd edn. New York: Wiley.
Deb, K. (1996). "Genetic Algorithms for Function Optimisation." Genetic Algorithms and Soft Computing 8, 4–31.
De Jong, K. (1993). "Genetic Algorithms are NOT Function Optimisers." In D. Whitley (ed.), Foundations of Genetic Algorithms, Vol. 2. San Mateo, CA: Morgan Kaufmann, pp. 5–17.
Dowsland, K. (1998). "Nurse Scheduling with Tabu Search and Strategic Oscillation." European Journal of Operational Research 106, 393–407.
Dowsland, K. and J. Thompson. (2000). "Nurse Scheduling with Knapsacks, Networks and Tabu Search." Journal of the Operational Research Society 51, 825–833.
Fogel, D. (1998). Evolutionary Computation: The Fossil Record. IEEE Press.
Fuller, E. (1998). "Tackling Scheduling Problems Using Integer Programming." Master Thesis, University of Wales Swansea, United Kingdom.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning. Reading, MA: Addison-Wesley.
Holland, J. (1976). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
Hung, R. (1995). "Hospital Nurse Scheduling." Journal of Nursing Administration, 21–23.
Lehmann, E.L. (1975). Nonparametrics: Statistical Methods Based on Ranks. San Francisco, CA: Holden-Day.
Michalewicz, Z. (1995). "A Survey of Constraint Handling Techniques in Evolutionary Computation Methods." In Proceedings of the 4th Annual Conference on Evolutionary Programming, pp. 135–155.
Sitompul, D. and S. Randhawa. (1990). "Nurse Scheduling Models: A State-of-the-Art Review." Journal of the Society of Health Systems 2, 62–72.
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Aickelin, U., White, P. Building Better Nurse Scheduling Algorithms. Annals of Operations Research 128, 159–177 (2004). https://doi.org/10.1023/B:ANOR.0000019103.31340.a6
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DOI: https://doi.org/10.1023/B:ANOR.0000019103.31340.a6