An estimation of distribution algorithm for nurse scheduling

Annals of Operations Research

Volumn 155, Issue 1, 2007, Pages 289-309

  Aickelin, Uwe ^a   Li, Jingpeng ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Bayesian network; Estimation of distribution algorithms; Nurse scheduling

Indexed keywords

EID: 34548254543     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-007-0214-0     Document Type: Article

References (25)

1. Aickelin, U. (2002). An indirect genetic algorithm for set covering problems. Journal of the Operational Research Society, 53, 1118-1126.
2. Aickelin, U., & Dowsland, K. (2000). Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. Journal of Scheduling, 3, 139-153.
3. Aickelin, U., & Dowsland, K. (2002). Enhanced direct and indirect genetic algorithm approaches for a mall layout and tenant selection problem. Journal of Heuristics, 8, 503-514.
4. Aickelin, U., & Dowsland, K. (2004). An indirect genetic algorithm for a nurse scheduling problem. Computers and Operations Research 31, 761-778.
5. Aickelin, U., & White, P. (2004). Building better nurse scheduling algorithms. Annals of Operations Research, 128, 159-177.
6. Bellanti, F., Carello, G., Della Croce, F., & Tadei, R. (2004). A greedy-based neighborhood search approach to a nurse rostering problem. European Journal of Operational Research, 153, 28-40.
7. Burke, E. K., De Causmaecker, P., & Vanden Berghe, G. (1999). A hybrid tabu search algorithm for the nurse rostering problem. In B. McKay et al. (Eds.), Lecture Notes in Artificial Intelligence: Vol. 1585. Simulated evolution and learning (pp. 187-194). Berlin: Springer.
8. Burke, E. K., Cowling, P., De Causmaecker, P., & Vanden Berghe, G. (2001). A memetic approach to the nurse rostering problem. Applied Intelligence, 15, 199-214.
9. Burke, E. K., De Causmaecker, P., Vanden Berghe, G., & Van Landeghem, H. (2004). The state of the art of nurse rostering. Journal of Scheduling, 7, 441-499.
10. Cheang, B., Li, H., Lim, A., & Rodrigues, B. (2003). Nurse rostering problems - a bibliographic survey. European Journal of Operational Research, 151, 447-460.
11. De Bonet, J. S., Isbell, C. L., & Viola, P. (1997). MIMC: finding optima by estimating probability densities. In M. C. Mozer et al. (Eds.), Advances in neural information processing systems (pp. 424). Cambridge: MIT Press.
12. Dowsland, K. A., & Thompson, J. M. (2000). Solving a nurse scheduling with knapsacks, networks and tabu search. Journal of Operational Research Society, 51, 825-833.
13. Edwards, D. (2000). Introduction to graphical modelling (2nd ed.). Berlin: Springer.
14. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley.
15. Heckerman, D. (1998). A tutorial on learning with Bayesian networks. In M. Jordan (Ed.), Learning in graphical models. Cambridge: MIT Press.
16. Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press.
17. Isken, M. W., & Hancock, W. (1991). A heuristic approach to nurse scheduling in hospital units with non-stationary, urgent demand, and a fixed staff size. Journal ofthe Society for Health Systems, 2, 24-41.
18. Jan, A., Yamamoto, M., & Ohuchi, A. (2000). Evolutionary algorithms for nurse scheduling problems. In Proceedings of the 2000 congress on evolutionary computation, San Diego (pp. 196-203).
19. Jaszkiewicz, A. (1997). A metaheuristic approach to multiple objective nurse scheduling. Foundations of Computing and Decision Sciences, 22, 169-184.
20. Jordan, M. I. (Ed.) (1999). Learning in graphical models. Cambridge: MIT Press.
21. Larranaga, P., & Lozano, J. A. (2001). Estimation of distribution algorithms. Dordrecht: Kluwer Academic.
22. Mühlenbein, H., & Mahnig, T. (1999). FDA - a scalable evolutionary algorithm for the optimization of additively decomposed functions. Evolutionary Computation, 7, 45-68.
23. Pearl, J. (1998). Probabilistic reasoning in intelligent systems: networks of plausible inference. Los Altos: Morgan Kaufmann.
24. Pelikan, M., & Goldberg, D. (2000). Research on the Bayesian optimization algorithms (IlliGAL report No 200010). University of Illinois.
25. Pelikan, M., Goldberg, D., & Cantu-Paz, E. (1999). BOA: the Bayesian optimization algorithm (IlliGAL report No 99003). University of Illinois.