Finding good nurse duty schedules: a case study | Journal of Scheduling Skip to main content

Advertisement

Springer Nature Link
Log in

Finding good nurse duty schedules: a case study

  • Published:
Journal of Scheduling Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • 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.

  • Aickelin, U., & Dowsland, K. A. (2003). An indirect genetic algorithm for a nurse scheduling problem. Computers and Operations Research, 31(5), 761–778.

    Article  Google Scholar 

  • Aickelin, U., & White, P. (2004). Building better nurse scheduling algorithms. Annals of Operations Research, 128, 159–177.

    Article  Google Scholar 

  • Alward, R. R., & Monk, T. H. (1994). Supporting shift workers. Journal of Nursing Administration, 24, 53–59.

    Article  Google Scholar 

  • Arthur, J. L., & Ravindran, A. (1981). A multiple objective nurse scheduling model. AIIE Transactions, 13(1), 55–60.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Bard, J. F., & Purnomo, H. W. (2005b). Preference scheduling for nurses using column generation. European Journal of Operational Research, 164(2), 510–534.

    Article  Google Scholar 

  • Bard, J. F., & Purnomo, H. W. (2007). Cyclic preference scheduling of nurses using a Lagrangian-based heuristic. Journal of Scheduling, 10(1), 5–23.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Belien, J., & Demeulemeester, E. (2007, in press). A branch-and-price approach for integrating nurse and surgery scheduling. European Journal of Operational Research.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Burke, E. K., Kendall, G., & Soubeiga, E. (2003). A tabu search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6), 451–470.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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).

  • Cheang, B., Li, H., Lim, A., & Rodrigues, B. (2003). Nurse rostering problems—a bibliographic survey. European Journal of Operational Research, 151(3), 447–460.

    Article  Google Scholar 

  • Chen, J. G., & Yeung, T. W. (1993). Hybrid expert-system approach to nurse scheduling. Computers in Nursing, 11(4), 183–190.

    Google Scholar 

  • 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).

  • 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.

    Chapter  Google Scholar 

  • 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 

  • Díaz, J. A., & Fernández, E. (2001). A tabu search heuristic for the generalized assignment problem. European Journal of Operational Research, 132, 22–38.

    Article  Google Scholar 

  • Dige, P., & Lund, C. (1992). Timetabling by simulated annealing (pp. 151–174). Lyngby: The Institute of Mathematical Statistics and Operations Research.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Ikegami, A., & Niwa, A. (2004). A subproblem—centric model and approach to the nurse scheduling problem. Mathematical Programming, 97(3), 517–541.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • Jaumard, B., Semet, F., & Vovor, T. (1998). A generalized linear programming model for nurse scheduling. European Journal of Operational Research, 107, 1–18.

    Article  Google Scholar 

  • Kragelund, L. V. (1997). Solving a timetabling problem using hybrid genetic algorithms. Software—Practice and Experience, 27(10), 1121–1134.

    Article  Google Scholar 

  • 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.

  • 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 

  • 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 

  • 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).

  • Maenhout, B., & Vanhoucke, M. (2005). An electromagnetism meta-heuristic for the nurse scheduling problem. Vlerick Leuven Gent Management School.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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).

  • Michalewics, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd ed.). Berlin: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Moz, M., & Pato, M. V. (2007). A genetic algorithm approach to a nurse rerostering problem. Computers and Operations Research, 34, 667–691.

    Article  Google Scholar 

  • 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 

  • Ozkarahan, I., & Bailey, J. E. (1988). goal programming model subsystem of a flexible nurse scheduling support system. IIE Transactions, 20(5), 306–316.

    Article  Google Scholar 

  • Ozkarahan, I. (1989). A flexible nurse scheduling support system. Computer Methods and Programs in Biomedicine, 30(2–3), 145–153.

    Article  Google Scholar 

  • Parr, D., & Thompson, J. M. (2007, to appear). Solving the multi-objective nurse scheduling problem with a weighted cost function. Annals of Operations Research.

  • 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.

  • 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 

  • Price, E. M. (1981). Seven days on and seven days off. American Journal of Nursing, 1142–1143.

  • 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

  • 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 

  • Roosenbloom, E. S., & Goertzen, N. F. (1987). Cyclic nurse scheduling. European Journal of Operational Research, 31(1), 19–23.

    Article  Google Scholar 

  • 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 

  • 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 

  • Valouxis, C., & Housos, E. (2000). Hybrid optimization techniques for the workshift and rest assignment of nursing personnel. Artificial Intelligence in Medicine, 20, 155–175.

    Article  Google Scholar 

  • 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 

  • Warner, D. M. (1976b). Scheduling nursing personnel according to nursing preference: a mathematical programming model. Operations Research, 24, 842–856.

    Google Scholar 

  • Warner, D. M., & Prawda, J. (1972). A mathematical programming model for scheduling nursing personnel in a hospital. Management Science, 19(4), 411–422.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

Download references

Author information

Authors and Affiliations

  1. Applied Mathematics Division, Department of Mathematical Sciences, Stellenbosch University, Private Bag X1, Matieland, 7602, Republic of South Africa

    M. J. Bester & I. Nieuwoudt

  2. Department of Logistics, Stellenbosch University, Private Bag X1, Matieland, 7602, Republic of South Africa

    Jan H. Van Vuuren

Authors
  1. M. J. Bester
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. I. Nieuwoudt
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jan H. Van Vuuren
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jan H. Van Vuuren.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bester, M.J., Nieuwoudt, I. & Van Vuuren, J.H. Finding good nurse duty schedules: a case study. J Sched 10, 387–405 (2007). https://doi.org/10.1007/s10951-007-0035-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10951-007-0035-7

Keywords