Journal of Scheduling

, Volume 10, Issue 6, pp 387–405 | Cite as

Finding good nurse duty schedules: a case study

Article
First Online:

Abstract

Constructing duty schedules for nurses at large hospitals is a difficult problem. The objective is usually to ensure that there is always sufficient staff on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated fairly. The problem is typically solved via mixed integer programming or heuristic (local) search methods in the operations research literature. In this paper the problem is solved using a tabu search approach as a case study at Stikland Hospital, a large psychiatric hospital in the South African Western Cape, for which a computerized decision support system with respect to nurse scheduling was developed. This decision support system, called NuRoDSS (short for Nurse Rostering Decision Support System) is described in some detail.

Keywords

Heuristic duty scheduling Tabu search Nurse rostering 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aickelin, U., Burke, E. K., & Li, J. (2007, in press). An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering. Journal of the Operational Research Society. Google Scholar
  2. Aickelin, U., & Dowsland, K. A. (2003). An indirect genetic algorithm for a nurse scheduling problem. Computers and Operations Research, 31(5), 761–778. CrossRefGoogle Scholar
  3. Aickelin, U., & White, P. (2004). Building better nurse scheduling algorithms. Annals of Operations Research, 128, 159–177. CrossRefGoogle Scholar
  4. Alward, R. R., & Monk, T. H. (1994). Supporting shift workers. Journal of Nursing Administration, 24, 53–59. CrossRefGoogle Scholar
  5. Arthur, J. L., & Ravindran, A. (1981). A multiple objective nurse scheduling model. AIIE Transactions, 13(1), 55–60. Google Scholar
  6. Azaiez, M. N., & Al Sharif, S. S. (2005). A 0-1 goal programming model for nurse scheduling. Computers and Operations Research, 32(3), 491–507. CrossRefGoogle Scholar
  7. Bard, J. F., & Purnomo, H. W. (2004). Real-time scheduling for nurses in response to demand fluctuations and personnel shortages. In M. Trick & E. Burke (Eds.), Proceedings of the 5th international conference on the practice and theory of automated timetabling (pp. 67–87). Pittsburgh. Google Scholar
  8. Bard, J. F., & Purnomo, H. W. (2005a). A column generation-based approach to solve the preference scheduling problem for nurses with downgrading. Socio-Economic Planning Sciences, 39(3), 193–213. CrossRefGoogle Scholar
  9. Bard, J. F., & Purnomo, H. W. (2005b). Preference scheduling for nurses using column generation. European Journal of Operational Research, 164(2), 510–534. CrossRefGoogle Scholar
  10. Bard, J. F., & Purnomo, H. W. (2007). Cyclic preference scheduling of nurses using a Lagrangian-based heuristic. Journal of Scheduling, 10(1), 5–23. CrossRefGoogle Scholar
  11. Beddoe, G. R., & Petrovic, S. (2003). A novel approach to finding feasible solutions to personnel rostering problems. In Proceedings of the 14th annual conference of the production and operations management society (POM), Savannah, Georgia. Google Scholar
  12. Beddoe, G. R., & Petrovic, S. (2006). Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering. European Journal of Operational Research, 175(2), 649–671. CrossRefGoogle Scholar
  13. Belien, J., & Demeulemeester, E. (2007, in press). A branch-and-price approach for integrating nurse and surgery scheduling. European Journal of Operational Research. Google Scholar
  14. Bellanti, F., Carello, G., Croce, F. D., & Tadei, R. (2004). A greedy-based neighborhood search approach to a nurse rostering problem. European Journal of Operational Research, 153, 28–40. CrossRefGoogle Scholar
  15. Berrada, I., Ferland, J. A., & Michelon, P. (1996). A multi-objective approach to nurse scheduling with both hard and soft constraints. Economic Planning Sciences, 30(3), 183–193. CrossRefGoogle Scholar
  16. Bertels, S., & Fahle, T. (2006). A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem. Computers and Operations Research, 33(10), 2866–2890. CrossRefGoogle Scholar
  17. Burke, E. K., de Causmaecker, P., Petrovic, S., & van den Berghe, G. (2002a). Fitness evaluation for nurse scheduling problems. Proceedings of the 2001 conference on evolutionary computation (Vol. 2, pp. 1139–1146). Google Scholar
  18. Burke, E. K., Kendall, G., & Soubeiga, E. (2003). A tabu search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6), 451–470. CrossRefGoogle Scholar
  19. Burke, E. K., de Causmaecker, P., van den Berghe, G., & van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7(6), 441–499. CrossRefGoogle Scholar
  20. Burke, E. K., Curtois, T., Post, G., Qu, R., & Veltman, B. (2005). A hybrid heuristic ordering and variable neighborhood search for the nurse rostering problem (Technical Report NOTTCS-TR-2005-3). School of Computer Science & IT, University of Nottingham. Google Scholar
  21. Burke, E. K., de Causmaecker, P., Petrovic, S., & Van den Berghe, G. (2006). Metaheuristics for handling time interval coverage constraints in nurse scheduling. Applied Artificial Intelligence, 20(3). Google Scholar
  22. Cheang, B., Li, H., Lim, A., & Rodrigues, B. (2003). Nurse rostering problems—a bibliographic survey. European Journal of Operational Research, 151(3), 447–460. CrossRefGoogle Scholar
  23. Chen, J. G., & Yeung, T. W. (1993). Hybrid expert-system approach to nurse scheduling. Computers in Nursing, 11(4), 183–190. Google Scholar
  24. Cheng, B. M. W., Lee, J. H. M., & Wu, J. C. K. (1996). A constraint-based nurse rostering system using a redundant modeling approach. Proceedings 8th IEEE international conference on tools with artificial intelligence (pp. 140–148). Google Scholar
  25. Cipriano, R., Gaspero, L. D., & Dovier, A. (2006). Hybrid approaches for rostering: a case study in the integration of constraint programming and local search. In M. J. B. Aguilera (Ed.), Lecture notes in computer science : Vol. 4030. 3rd international workshop on hybrid metaheurstics. Berlin: Springer. CrossRefGoogle Scholar
  26. Cowling, P., Kendall, G., & Soubeiga, E. (2002). Hyperheuristics: a robust optimisation method applied to nurse scheduling. In Lecture notes in computer science : Vol. 2439. Parallel problem solving techniques from nature (pp. 851–860). Berlin: Springer. Google Scholar
  27. Díaz, J. A., & Fernández, E. (2001). A tabu search heuristic for the generalized assignment problem. European Journal of Operational Research, 132, 22–38. CrossRefGoogle Scholar
  28. Dige, P., & Lund, C. (1992). Timetabling by simulated annealing (pp. 151–174). Lyngby: The Institute of Mathematical Statistics and Operations Research. Google Scholar
  29. Dowsland, K. A., & Thompson, J. M. (2000). Solving a nurse scheduling problem with knapsack, networks and tabu search. Journal of the Operational Research Society, 51(7), 825–833. CrossRefGoogle Scholar
  30. Ernst, A. T., Jiang, H., Krishnamoorthy, M., Owens, B., & Sier, D. (2004a). An annotated bibliography of personnel scheduling and rostering. Annals of Operations Research, 127, 21–144. CrossRefGoogle Scholar
  31. Ernst, A. T., Jiang, H., Krishnamoorthy, M., & Sier, D. (2004b). Staff scheduling and rostering: a review of applications, methods and models. European Journal of Operational Research, 153(1), 3–27. CrossRefGoogle Scholar
  32. Fung, S. K. L., Leung, H., & Lee, J. H. M. (2005). Guided complete search for nurse rostering problems. In Proceedings of the 17th IEEE international conference on tools with artificial intelligence (ICTAI05) (pp. 1082–3409). New York: IEEE. Google Scholar
  33. Gascon, V., Villeneuve, S., Michelon, P., & Ferland, J. A. (2000). Scheduling the flying squad nurses of a hospital using a multi-objective programming model. Annals of Operations Research, 96, 149–166. CrossRefGoogle Scholar
  34. Glover, F., & Laguna, M. (1993). Tabu search. In C. Reeves (Ed.), Modern Heuristic techniques for combinatorial problems. Oxford: Backwell Scientific Publications (Chap. 3). Google Scholar
  35. Gutjahr, W. J., & Rauner, M. S. (2007). An aco algorithm for a dynamic regional nurse-scheduling problem in Austria. Computers and Operations Research, 34, 642–666. CrossRefGoogle Scholar
  36. Ikegami, A., & Niwa, A. (2004). A subproblem—centric model and approach to the nurse scheduling problem. Mathematical Programming, 97(3), 517–541. CrossRefGoogle Scholar
  37. Inoue, T., Furuhashi, T., Fujii, M., Maeda, H., & Takaba, M. (1999). Development of nurse scheduling support system using interactive EA. In Systems, man, and cybernetics. IEEE SMC ’99 Conference Proceedings, 5, 533–537. Google Scholar
  38. Inoue, T., Furuhashi, T., Maeda, H., & Takaba, M. (2003). A proposal of combined method of evolutionary algorithm and heuristics for nurse scheduling support system. IEEE Transactions on Industrial Electronics, 50(5), 833–838. CrossRefGoogle Scholar
  39. Irken, M. W., & Hancock, W. M. (1991). A heuristic approach to nurse scheduling in hospital units with non-stationary, urgent demand, and a fixed staff size. Journal of the Society for Health Systems, 2(2), 24–41. Google Scholar
  40. Jan, A., Yamamoto, M., & Ohuchi, A. (2000). Evolutionary algorithms for nurse scheduling problem. Proceedings of the 2000 Conference on Evolutionary Computation, 1, 196–203. Google Scholar
  41. Jaumard, B., Semet, F., & Vovor, T. (1998). A generalized linear programming model for nurse scheduling. European Journal of Operational Research, 107, 1–18. CrossRefGoogle Scholar
  42. Kragelund, L. V. (1997). Solving a timetabling problem using hybrid genetic algorithms. Software—Practice and Experience, 27(10), 1121–1134. CrossRefGoogle Scholar
  43. Li, J., & Aickelin, U. (2003). A Bayesian optimization algorithm for the nurse scheduling problem. In Proceedings of 2003 congress on evolutionary computation (CEC2003) (pp. 2149–2156). Canberra. Google Scholar
  44. Li, J., & Aickelin, U. (2004). The application of Bayesian optimization and classifier systems in nurse scheduling. In Lecture notes in computer science : Vol. 3242. Proceedings of the 8th international conference on parallel problem solving from nature (PPSN VIII) (pp. 581–590). Berlin: Springer. Google Scholar
  45. Li, J., & Aickelin, U. (2006). BOA for Nurse Scheduling, in scalable optimization via probabilistic modeling. In M. Pelican, K. Sastry & E. Cant-Paz (Eds.), From algorithms to applications (pp. 315–332). Berlin: Springer. Google Scholar
  46. Li, H., Lim, A., & Rodrigues, B. (2003). A hybrid AI approach for nurse rostering problem. In Proceedings of the 2003 ACM symposium on applied computing (pp. 730–735). Google Scholar
  47. Maenhout, B., & Vanhoucke, M. (2005). An electromagnetism meta-heuristic for the nurse scheduling problem. Vlerick Leuven Gent Management School. Google Scholar
  48. Maenhout, B., & Vanhoucke, M. (2006). New computational results for the nurse scheduling problem: a scatter search algorithm. Lecture Notes in Computer Science, 3906, 159–170. CrossRefGoogle Scholar
  49. Mason, A. J. (1999). Solution methods for cyclic roster construction, [Online], [cited 2001, Aug 10], Available from: http://www.esc.aukland.ac.nz/Mason, Auckland, pp. 1–28.
  50. Mason, A. J., & Smith, M. C. (1998). A nested column generator for solving rostering problems with integer programming. International conference on optimisation: techniques and applications, Auckland (pp. 1–8). Google Scholar
  51. Michalewics, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd ed.). Berlin: Springer. Google Scholar
  52. Millar, H. H., & Kigaru, M. (1998). Cyclic and non-cyclic scheduling of 12h shift nurses by network programming. European Journal of Operational Research, 104(2), 582–592. CrossRefGoogle Scholar
  53. Moz, M., & Pato, M. V. (2004). Solving the problem of rerostering nurse schedules with hard constraints: new multicommodity flow models. Annals of Operations Research, 128, 179–197. CrossRefGoogle Scholar
  54. Moz, M., & Pato, M. V. (2007). A genetic algorithm approach to a nurse rerostering problem. Computers and Operations Research, 34, 667–691. CrossRefGoogle Scholar
  55. Osogami, T., & Ismai, H. (2000). Classification of various neighborhood operations for the nurse scheduling problem in algorithms and computation. In Lecture notes in computer science : Vol. 1969. 11th international conference, ISAAC 2000 (pp. 72–83). Taipei: Springer. Google Scholar
  56. Ozkarahan, I., & Bailey, J. E. (1988). goal programming model subsystem of a flexible nurse scheduling support system. IIE Transactions, 20(5), 306–316. CrossRefGoogle Scholar
  57. Ozkarahan, I. (1989). A flexible nurse scheduling support system. Computer Methods and Programs in Biomedicine, 30(2–3), 145–153. CrossRefGoogle Scholar
  58. Parr, D., & Thompson, J. M. (2007, to appear). Solving the multi-objective nurse scheduling problem with a weighted cost function. Annals of Operations Research. Google Scholar
  59. Petrovic, S., Beddoe, G. R., & Vanden Berghe, G. (2002). Case-based reasoning in employee rostering: learning repair strategies from domain experts (Technical Report). Automated Scheduling Optimisation and Planning Research Group, School of Computer Science and Information Technology, University of Nottingham. Google Scholar
  60. Petrovic, S., Beddoe, G. R., & Vanden Berghe, G. (2003). Storing and adapting repair experiences in employee rostering. In E. K. Burke & P. De Causmaecker (Eds.), Lecture notes in computer science : Vol. 2740. Selected papers from the 4th international conference on the practice and theory of automated timetabling (PATAT 2002) (pp. 149–166). Berlin: Springer. Google Scholar
  61. Price, E. M. (1981). Seven days on and seven days off. American Journal of Nursing, 1142–1143. Google Scholar
  62. Punnakitikashem, P., Rosenberger, J. M., & Behan, D. B. (2005). Stochastic programming for nurse assignment (Technical Report COSMOS 05-01). The University of Texas at Arlington. Arlington Google Scholar
  63. Richard, C., & Kavois, J. A. (1992). Nurse staffing and scheduling: past solutions and future directions. Journal of the Society for Health Systems, 3, 75–81. Google Scholar
  64. Roosenbloom, E. S., & Goertzen, N. F. (1987). Cyclic nurse scheduling. European Journal of Operational Research, 31(1), 19–23. CrossRefGoogle Scholar
  65. Sitompul, D., & Randhawa, S. U. (1990). Nurse scheduling models: a state-of-the-art review. Journal of the Society of Health Systems, 2, 62–72. Google Scholar
  66. Spencer, K. L., Leung, F. H.-F., & Lee, J. H. M. (2005). Guided complete search for nurse rostering problems. In Proceedings of the 17th IEEE international conference on tools with artificial intelligence (ICTAI 05). Los Altos: IEEE. Google Scholar
  67. 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
  68. Warner, D. M. (1976a). Computer-aided system for nurse scheduling. In J. R. Griffith, H. M. Hancock & F. C. Munson (Eds.), Cost control in hospitals. The University of Michigan, Ann Arbor: Health Administration Press. Google Scholar
  69. Warner, D. M. (1976b). Scheduling nursing personnel according to nursing preference: a mathematical programming model. Operations Research, 24, 842–856. Google Scholar
  70. Warner, D. M., & Prawda, J. (1972). A mathematical programming model for scheduling nursing personnel in a hospital. Management Science, 19(4), 411–422. CrossRefGoogle Scholar
  71. Warner, D. M., Keller, B. J., & Martel, S.H. (1991). Automated nurse scheduling. Journal of the Society of Health Systems, 2(2), 66–80. Google Scholar
  72. Weil, G., Heus, K., Francois, P., & Poujade, M. (1995). Constraint programming for nurse scheduling. IEEE Engineering in Medicine and Biology Magazine, 14(4), 417–422. CrossRefGoogle Scholar
  73. Wong, G. Y. C., & Chun, H. W. (2003). Nurse rostering using constraint programming and meta-level reasoning. In Developments in applied artificial intelligence: 16th international conference on industrial and engineering applications of artificial intelligence and expert systems (pp. 712–721). Loughborough: Springer. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • M. J. Bester
    • 1
  • I. Nieuwoudt
    • 1
  • Jan H. Van Vuuren
    • 2
  1. 1.Applied Mathematics Division, Department of Mathematical SciencesStellenbosch UniversityMatielandRepublic of South Africa
  2. 2.Department of LogisticsStellenbosch UniversityMatielandRepublic of South Africa

Personalised recommendations