Advertisement

Nurse Rostering Using Modified Harmony Search Algorithm

  • Mohammed A. Awadallah
  • Ahamad Tajudin Khader
  • Mohammed Azmi Al-Betar
  • Asaju La’aro Bolaji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)

Abstract

In this paper, a Harmony Search Algorithm (HSA) is adapted for Nurse Rostering Problem (NRP). HSA is a global optimization method derived from a musical improvisation process which has been successfully tailored for several optimization domains. NRP is a hard combinatorial scheduling problem of assigning given shifts to given nurses. Using a dataset established by International Nurse Rostering Competition 2010 of sprint dataset that has 10-early, 10-late, 10-hidden, and 3-hint. The proposed method achieved competitively comparable results.

Keywords

Harmony Search Soft Constraint Harmony Search Algorithm Harmony Memory Pitch Adjustment Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aickelin, U., Dowsland, K.: An indirect genetic algorithm for a nurse-scheduling problem. Computers & Operations Research 31(5), 76–778 (2004)CrossRefzbMATHGoogle Scholar
  2. 2.
    Al-Betar, M., Khader, A.: A hybrid harmony search for university course timetabling. In: Proceedings of the 4nd Multidisciplinary Conference on Scheduling: Theory and Applications (MISTA 2009), Dublin, Ireland, pp. 10–12 (August 2009)Google Scholar
  3. 3.
    Al-Betar, M., Khader, A.: A harmony search algorithm for university course timetabling. Annals of Operations Research, 1–29 (2008)Google Scholar
  4. 4.
    Al-Betar, M., Khader, A., Nadi, F.: Selection mechanisms in memory consideration for examination timetabling with harmony search. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1203–1210. ACM (2010)Google Scholar
  5. 5.
    Alatas, B.: Chaotic harmony search algorithms. Applied Mathematics and Computation 216(9), 2687–2699 (2010)CrossRefzbMATHGoogle Scholar
  6. 6.
    Bilgin, B., De Causmaecker, P., Rossie, B., Vanden Berghe, G.: Local search neighbourhoods for dealing with a novel nurse rostering model. Annals of Operations Research, 1–25 (2011)Google Scholar
  7. 7.
    Brusco, M., Jacobs, L.: Cost analysis of alternative formulations for personnel scheduling in continuously operating organizations. European Journal of Operational Research 86(2), 249–261 (1995)CrossRefzbMATHGoogle Scholar
  8. 8.
    Burke, E., Cowling, P., De Causmaecker, P., Berghe, G.: A memetic approach to the nurse rostering problem. Applied Intelligence 15(3), 199–214 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Burke, E., Curtois, T., Post, G., Qu, R., Veltman, B.: A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem. European Journal of Operational Research 188(2), 330–341 (2008)CrossRefzbMATHGoogle Scholar
  10. 10.
    Burke, E., Curtois, T., Qu, R., Berghe, G.: A scatter search methodology for the nurse rostering problem. Journal of the Operational Research Society 61(11), 1667–1679 (2009)CrossRefGoogle Scholar
  11. 11.
    Burke, E., De Causmaecker, P., Berghe, G., Van Landeghem, H.: The state of the art of nurse rostering. Journal of Scheduling 7(6), 441–499 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Burke, E., De Causmaecker, P., Vanden Berghe, G.: A hybrid tabu search algorithm for the nurse rostering problem. Simulated Evolution and Learning, 187–194 (1999)Google Scholar
  13. 13.
    Burke, E., Li, J., Qu, R.: A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems. European Journal of Operational Research 203(2), 484–493 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Cheang, B., Li, H., Lim, A., Rodrigues, B.: Nurse rostering problems - a bibliographic survey. European Journal of Operational Research 151(3), 447–460 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Geem, Z.: Optimal cost design of water distribution networks using harmony search. Engineering Optimization 38(3), 259–277 (2006)CrossRefGoogle Scholar
  16. 16.
    Geem, Z.: Harmony search applications in industry. Soft Computing Applications in Industry, 117–134 (2008)Google Scholar
  17. 17.
    Geem, Z., Kim, J., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  18. 18.
    Geem, Z., Lee, K., Park, Y.: Application of harmony search to vehicle routing. American Journal of Applied Sciences 2(12), 1552–1557 (2005)CrossRefGoogle Scholar
  19. 19.
    Geem, Z., Sim, K.: Parameter-setting-free harmony search algorithm. Applied Mathematics and Computation, 3881–3889 (2010)Google Scholar
  20. 20.
    Geem, Z., Tseng, C., Park, Y.: Harmony search for generalized orienteering problem: best touring in china. Advances in Natural Computation, 741–750 (2005)Google Scholar
  21. 21.
    Gutjahr, W., Rauner, M.: An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Computers & Operations Research 34(3), 642–666 (2007)CrossRefzbMATHGoogle Scholar
  22. 22.
    Kaveh, A., Talatahari, S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers & Structures 87(5-6), 267–283 (2009)CrossRefGoogle Scholar
  23. 23.
    Lee, K., Geem, Z.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering 194(36-38), 3902–3933 (2005)CrossRefzbMATHGoogle Scholar
  24. 24.
    Lee, K., Geem, Z., Lee, S., Bae, K.: The harmony search heuristic algorithm for discrete structural optimization. Engineering Optimization 37(7), 663–684 (2005)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Maenhout, B., Vanhoucke, M.: An electromagnetic meta-heuristic for the nurse scheduling problem. Journal of Heuristics 13(4), 359–385 (2007)CrossRefGoogle Scholar
  26. 26.
    Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation 188(2), 1567–1579 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Millar, H., Kiragu, M.: Cyclic and non-cyclic scheduling of 12 h shift nurses by network programming. European Journal of Operational Research 104(3), 582–592 (1998)CrossRefzbMATHGoogle Scholar
  28. 28.
    Pan, Q., Suganthan, P., Liang, J., Tasgetiren, M.: A local-best harmony search algorithm with dynamic subpopulations. Engineering Optimization 42(2), 101–117 (2010)CrossRefGoogle Scholar
  29. 29.
    Pan, Q., Suganthan, P., Tasgetiren, M., Liang, J.: A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation 216(3), 830–848 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Wren, A.: Scheduling, Timetabling and Rosteringa Special Relationship? In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 46–75. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  31. 31.
    Zhao, S., Suganthan, P., Pan, Q., Tasgetiren, M.: Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Systems with Applications, 3735–3742 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohammed A. Awadallah
    • 1
  • Ahamad Tajudin Khader
    • 1
  • Mohammed Azmi Al-Betar
    • 1
    • 2
  • Asaju La’aro Bolaji
    • 1
  1. 1.School of Computer SciencesUniversiti Sains Malaysia (USM)PulauMalaysia
  2. 2.Department of Computer ScienceAl-zaytoonah UniversityAmmanMalaysia

Personalised recommendations