Advertisement

Annals of Operations Research

, Volume 155, Issue 1, pp 279–288 | Cite as

Solving the multi-objective nurse scheduling problem with a weighted cost function

  • D. Parr
  • J. M. Thompson
Article

Abstract

The primary objective of the nurse scheduling problem is to ensure there are sufficient nurses on each shift. There are also a number of secondary objectives designed to make the schedule more pleasant. Neighbourhood search implementations use a weighted cost function with the weights dependent on the importance of each objective. Setting the weights on binding constraints so they are satisfied but still allow the search to find good solutions is difficult. This paper compares two methods for overcoming this problem, SAWing and Noising with simulated annealing and demonstrates that Noising produces better schedules.

Keywords

Nurse scheduling Meta-heuristic Simulated annealing SAWing Noising 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdennadher, A., & Schlenker, H. (1999). Nurse scheduling using constraint logic programming. In Eleventh annual conference on innovative applications of artificial intelligence (IAAI). Google Scholar
  2. Abramson, D. (1991). Constructing school timetables using simulated annealing: sequential and parallel algorithms. Management Science, 37, 98–113. Google Scholar
  3. Aickelin, U., & Dowsland, K. A. (2004). An indirect genetic algorithm for a nurse scheduling problem. Computers and Operations Research, 31, 761–778. CrossRefGoogle Scholar
  4. Baker, K. (1974). Scheduling a full time workforce to meet cyclic staffing requirements. Management Science, 20, 1561–1568. Google Scholar
  5. Baker, K., & Magazine, M. (1977). Workforce scheduling with cyclic demands and day off constraints. Management Science, 24, 161–167. CrossRefGoogle Scholar
  6. Bellanti, F., Carello, G., Della Croce, F., & Tadei, R. (2004). A greedy based neighbourhood search approach to a nurse rostering problem. European Journal of Operational Research, 153, 28–40. CrossRefGoogle Scholar
  7. Berrada, I., Ferland, J. A., & Michelon, P. (1996). A multi-objective approach to nurse scheduling with both hard and soft constraints. Socio-Economic Planning Science, 30, 183–193. CrossRefGoogle Scholar
  8. Burke, E., De Causmaecker, P., & Vanden Berghe, G. (1998). A hybrid tabu search algorithm for the nurse rostering problem. In Lecture notes in artificial intelligence: Vol. 1585. Simulated evolution and learning, selected papers of SEAL, Canberra (pp. 187–194). Google Scholar
  9. Burke, E., De Causmaecker, P., Petrovic, S., & Vanden Berghe, G. (2003). Variable neighbourhood search for nurse rostering problem. In M. G. C. Resende & J. P. de Sousa (Eds.), Combinatorial optimization book series. Metaheuristics: computer decision-making (pp. 153–172). Dordrecht: Kluwer, Chapter 7. Google Scholar
  10. Burke, E., De Causmaecker, P., Vanden Berghe, G., & Van Landeghem, H. (2004). The state of the art of nurse rostering. The Journal of Scheduling, 7, 441–499. CrossRefGoogle Scholar
  11. Charon, I., & Hurdy, O. (1993). The noising method: a new method for combinatorial optimization. Operations Research Letters, 14, 133–137. CrossRefGoogle Scholar
  12. Charon, I., & Hurdy, O. (2001). The noising methods: a generalization of some metaheuristics. European Journal of Operational Research, 135, 86–101. CrossRefGoogle Scholar
  13. Cheng, B. M. W., Lee, J. H. M., & Wu, J. C. K. (1996). A nurse rostering system using constraint programming and redundant modelling. Technical report, Department of Computer Science and Engineering at The Chinese University of Hong Kong. Google Scholar
  14. Dowsland, K. A. (1993). Simulated annealing. In C. Reeves (Ed.), Modern heuristic techniques for combinatorial problems (pp. 20–63). Oxford: Blackwell. Google Scholar
  15. Dowsland, K. A. (1998). Nurse scheduling with tabu search and strategic oscillation, European Journal of Operational Research, 393–407. Google Scholar
  16. Dowsland, K. A., & Thompson, J. M. (2000). Nurse scheduling with knapsacks, networks and tabu search. Journal of the Operational Research Society, 825–833. Google Scholar
  17. Eiben, A. E., & van Hemert, J. I. (1999). SAW-ing EAs: adapting the fitness function for solving constrained problems. In New ideas in optimization (pp. 389–402). London: McGraw-Hill, Chapter 26. Google Scholar
  18. Frances, M. N. (1966). Implementing a program of cyclical scheduling of nursing personnel. Hospitals, 108–125, July 16. Google Scholar
  19. Glover, F. (1989). Tabu search. Part I. ORSA Journal on Computing, 190–206. Google Scholar
  20. Howell, J. P. (1966). Cyclical scheduling of nursing personnel. Hospitals, 77–85, January 16. Google Scholar
  21. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 671–680. Google Scholar
  22. Lundy, M., & Mees, A. (1986). Convergence of an annealing algorithm. Mathematical Programming, 111–124. Google Scholar
  23. Thompson, J. M., & Dowsland, K. A. (1996). Variants of simulated annealing for the examination timetabling problem. Annals of Operations Research, 63, 105–128. CrossRefGoogle Scholar
  24. Valouxis, C., & Housos, E. (2000). Hybrid optimization techniques for the workshift and rest assignment of nursing personnel. Artificial Intelligence in Medicine, 20, 155–175. CrossRefGoogle Scholar
  25. Wright, M. (1996). School timetabling using heuristic search. Journal of the Operational Research Society, 47, 347–357. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.School of MathematicsCardiff UniversityCardiffUK

Personalised recommendations