A greedy double swap heuristic for nurse scheduling


Murphy Choy and Michelle Cheong


One of the key challenges of nurse scheduling problem (NSP) is the number of constraints placed on preparing the timetable, both from the regulatory requirements as well as the patients’ demand for the appropriate nursing care specialists. In addition, the preferences of the nursing staffs related to their work schedules add another dimension of complexity. Most solutions proposed for solving nurse scheduling involve the use of mathematical programming and generally considers only the hard constraints. However, the psychological needs of the nurses are ignored and this resulted in subsequent interventions by the nursing staffs to remedy any deficiency and often results in last minute changes to the schedule. In this paper, we present a staff preference optimization framework solved with a greedy double swap heuristic. The heuristic yields good performance in speed at solving the problem. The heuristic is simple and we will demonstrate its performance by implementing it on open source spreadsheet software.


DOI: j.msl.2012.06.021

Keywords: Nursing scheduling ,Mathematical programming Swapping algorithm ,Optimization framework

How to cite this paper:

, M & Cheong, M. (2012). A greedy double swap heuristic for nurse scheduling.Management Science Letters, 2(6), 2001-2010.


References

Aickelin, U., & Dowsland, K. (2004). An indirect genetic algorithm for a nurse-scheduling problem. Comput. Oper. Res., 31 (5), 761–778.

Azaiez, Al-Sharif, (2005). A 0-1 goal programming model for nurse scheduling. Computers & Operations Research, 32(3), 491-507.

Brusco, M., & Jacobs, L. (1993). A simulated annealing approach to the solution of flexible labour scheduling problems. The Journal of the Operational Research Society, 44 (12), 1191–1200.

Burke, E., Causmaecker, P., Berghe, G., & Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7, 441–499.

Chen, J.-G., & Yeung, T. (1993). Hybrid expert-system approach to nurse scheduling. Computers in Nursing, 11, 183–192.

Coello-Coello, C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287.

Dowsland, K. (1998). Nurse scheduling with tabu search and strategic oscillation. European Journal of Operational Research, 106 (2-3), 393–407.

Dowsland, K., & Thompson, J. (2000, Jul). Solving a nurse scheduling problem with knapsacks, networks and tabu search. The Journal of the Operational Research Society, 51 (7), 825–833.

Li, J., & Aickelin, U. (2003). A bayesian optimization algorithm for the nurse scheduling problem. in Proceedings of 2003 Congress on Evolutionary Computation, 2149-2156.

Okada, M., & Okada, M. (1988). Prolog-based system for nursing staff scheduling implemented on a personal computer. Computers and Biomedical Research, 21 (1), 53–63.

Osogami, T., & Imai, H. (2000). Classification of various neighborhood operations for the nurse scheduling problem. In ISAAC ’00: Proceedings of the 11th International Conference on Algorithms and Computation (pp. 72–83). London, UK: Springer-Verlag.

Pelikan, M., Goldberg, D. E., & Cant´u-Paz, E. (1999). BOA: The Bayesian optimization algorithm (Technical Report IlliGAL 99003). Urbana, IL: Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign.

Tien, J., & Kamiyama, A. (1982). On manpower scheduling algorithms. SIAM Review, 24 (3), 275–287.

Ying-Shiuan You, Tian-Li Yu, & Ta-Chun Lien. (2010). Psychological Preference-based Optimization Framework: An Evolutionary Computation Approach for Constrained Problems Involving Human Preference. Master Thesis, TEIL Working paper.