Solving a bi-objective nurse rerostering problem by using a utopic Pareto genetic heuristic

Journal of Heuristics

Volumn 14, Issue 4, 2008, Pages 359-374

  Pato, Margarida Vaz ^a,b   Moz, Margarida ^a,b  

^a ISEG LISBON SCHOOL OF ECONOMICS AND MANAGEMENT   (Portugal)

^b UNIVERSITY OF LISBON   (Portugal)

Author keywords

Bi objective heuristics; Genetic algorithms; Nurse scheduling; Rerostering

Indexed keywords

BISMUTH PLATING; HEALTH; HEURISTIC METHODS; HOSPITALS;

BI-OBJECTIVE HEURISTICS; COMPUTATIONAL EXPERIMENTS; GENETIC ALGORITHMS; HARD CONSTRAINTS; NURSE SCHEDULING; PARETO RANKING; REROSTERING; SOFT CONSTRAINTS;

NURSING;

EID: 49449086185     PISSN: 13811231     EISSN: 15729397     Source Type: Journal    
DOI: 10.1007/s10732-007-9040-4     Document Type: Article

References (26)

1. Aickelin, U., Dowsland, K.A.: An indirect genetic algorithm for a nurse scheduling problem. Comput. Oper. Res. 31(5), 761-778 (2004)
2. Burke, E.K., De Causmaecker, P., Vanden Berghe, G., Van Landerghem, H.: The state of the art of nurse rostering. J. Sched. 7(6), 411-499 (2004)
3. Burke, E.K., Cowling, P., De Causmaecker, P., Vanden Berghe, G.: A memetic approach to the nurse rostering problem. Appl. Intell. 15(3), 199-214 (2001)
4. Carrasco, M.P., Pato, M.V.: A multiobjective genetic algorithm for the class/teacher timetabling problem. In: Burke, E., Erben, W. (eds.) Practice and Theory of Automated Timetabling III: Third International Conference PATAT 2000, selected revised papers. Lecture Notes in Computer Science, vol. 2079, pp. 3-17. Springer, Berlin (2001)
5. Cheang, B., Li, H., Lim, A., Rodrigues, B.: Nurse rostering problems-a bibliographic survey. Eur. J. Oper. Res. 151(3), 447-460 (2003)
6. Collette, Y., Siarry, P.: Three new metrics to measure the convergence of the metaheuristics towards the Pareto frontier and the aesthetic of a set of solutions in biobjective optimization. Comput. Oper. Res. 32(4), 773-792 (2005)
7. Cumming, A., Paechter, B., Rankin, R.: Post-publication timetabling. In: Practice and Theory of Automated Timetabling III: Third International Conference PATAT 2000, extended abstracts, pp. 107-108, 2000
8. Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
9. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)
10. Deb, K., Pratap, A., Aggarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182-197 (2002)
11. Ernst, A.T., Jiang, H., Krishnamoorthy, M., Sier, D.: Staff scheduling and rostering: a review of applications, methods and models. Eur. J. Oper. Res. 153(1), 3-27 (2004)
12. Fonseca, C., Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. In: Evolutionary Multi-Criterion Optimization Conference EMO 2005, Guanajuato, Mexico, 2005
13. Gandibleux, X.: Journée de Travail PM20 Programmation Mathématique Multiobjectif, Tours (November 2000)
14. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco (1979)
15. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Massachusetts (1989)
16. Moz, M.: Técnicas de investigação operacional aplicadas a um problema de escalonamento de pessoal em contexto hospitalar. PhD Dissertation, Instituto Superior de Economia e Gestão, Universidade Técnica de Lisboa (2003)
17. Moz, M., Pato, M.V.: An integer multicommodity flow model applied to the rerostering of nurse schedules. Ann. Oper. Res. 119(1-4), 285-301 (2003)
18. Moz, M., Pato, M.V.: Solving the problem of rerostering nurse schedules with hard constraints: new multicommodity flow models. Ann. Oper. Res. 128(1-4), 179-197 (2004)
19. Moz, M., Pato, M.V.: A bi-objective network flow approach for nurse rerostering. In: Gouveia, L., Mourão, C. (eds.), Proceedings of the INOC 2005-International Network Optimization Conference, Lisbon, March 2005, B3.817
20. Moz, M., Pato, M.V.: A genetic algorithm approach to a nurse rerostering problem. Comput. Oper. Res. 34, 667-691 (2007)
21. Petrovic, S., Beddoe, G., Vanden Berghe, G.: Storing and adapting repair experiences in personnel rostering. In: Burke, E., De Causmaecker, P. (eds.) Practice and Theory of Automated Timetabling IV: Fourth International Conference PATAT 2002, selected revised papers. Lecture Notes in Computer Science, vol. 2740, pp. 148-165. Springer, Berlin (2002)
22. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette, J.J. (ed.) Genetic Algorithms and their Applications, Proceedings of the Third International Conference on Genetic Algorithms, pp. 93-100. Lawrence Erlbaum, Hillsdale (1985)
23. Silva, J.D.L., Burke, E.K., Petrovic, S.: An introduction to multiobjective metaheuristics for scheduling and timetabling. In: Gandibleux, X., Sevaux, M., Sörensen, K., T'kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation. Springer, Berlin (2004)
24. Tien, J.M., Kamiyama, A.: On manpower scheduling algorithms. SIAM Rev. 24(3), 275-287 (1982)
25. Zitzler, E., Laumanns, M., Bleuler, S.: A tutorial on multiobjective optimization. In: Gandibleux, X., Sevaux, M., Sörensen, K., T'kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation. Springer, Berlin (2004)
26. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report 103, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Zurich (2001)