Abstract
The goal of this research is to evolve nurse scheduling problem as a matured computational model towards optimization of multiple and conflicting objectives, the complex shift patterns, etc., in changing the environment. This work aims to find out an effective assignment of nurses to home patients as well as in-hospital patients based on patients’ varying demands over time according to their health status. It proposes nurse scheduling algorithms based on variable time quantum, wait time, context switch time, etc., in different situations when the environment becomes more constrained as well as unconstrained. It develops corresponding cost functions for assigning suitable nurses by considering the penalty cost, swapping cost of nurses. This paper proposes methods to utilize nurses by using nearest neighbour based similarity measure and combined genetic algorithm to generate feasible solutions. Finally, this paper implements the proposed algorithms and compares these with the other popular existing algorithms.
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References
Li, J., Aickelin, U.: Bayesian optimisation algorithm for nurse scheduling, scalable optimization via probabilistic modeling: from algorithms to applications. In: Pelikan, M., Sastry, K., Cantu-Paz, E. (eds.) Studies in Computational Intelligence (Chapter 17), pp. 315–332. Springer, Berlin (2006)
Aickelin, U., Downsland, K.A.: Exploiting problem structure in genetic algorithms approach to a nurse rostering problem. J. Sched. 31, 139–153 (2000)
Maenhout, B., Vanhoucke, M.: Comparison and hybridization of crossover operators for the nurse scheduling problem. Ann. Oper. Res. 159(1), 333–353 (2008). https://doi.org/10.1007/s10479-007-0268-z
Fonseca, G.H., Santos, H.G., Carrano, E.G.: Late acceptance hill-climbing for high school timetabling. J. Sched., 1–13 (2015). https://doi.org/10.1007/s10951-015-0458-5
Burke, E.K., Newall, J.P., Weare, R.F.: A memetic algorithm for university exam timetabling. genetic algorithms practice and theory of automated timetabling. Lecture Notes in Computer Science, vol. 1153, pp. 241–250. Springer, Berlin (2005)
Constantino, A.A., Landa-Silva, D., Melo, E.L., Xavier de Mendonc, D.F., Rizzato, D.B., Romão, W.: A heuristic algorithm based on multi assignment procedures for nurse scheduling. Ann. Oper. Res. 218(1), 165–183 (2014). https://doi.org/10.1007/s10479-013-1357-9
Brucker, P., Burke Edmund K., Curtois, T., Qu, R., Berghe, V.G.: A shift sequence based approach for nurse scheduling and a new benchmark dataset. J. Heuristics 16(4), 559–573 (2010)
Maenhout, B., Vanhoucke, M.: An electromagnetic meta-heuristic for the nurse scheduling problem. J. Heuristics 13(4), 359–385 (2007)
Ratnayaka, R.K.T., Wang, Z.J., Anamalamudi, S. and Cheng, S.: Enhanced greedy optimization algorithm with data warehousing for automated nurse scheduling system. E-Health Telecommun. Syst. Netw. 1, 43–48 (2012). http://dx.doi.org/10.4236/etsn.2012.14007
Elahipanah, M., Desaulniers, G., Lacasse-Guay, È.: A two-phase mathematical-programming heuristic for flexible assignment of activities and tasks to work shifts. J. Sched. 16(5), 443–460 (2013). https://doi.org/10.1007/s10951-013-0324-2
Alkan, A., Ozcan, E.: Memetic algorithms for timetabling. In: The 2003 Congress on Evolutionary Computation, CEC ‘03, vol. 3, pp. 1796–1802. IEEE (2003). https://doi.org/10.1109/cec.2003.1299890
Ko, Y.W., Kim, D.H., Jeong, M., Jeon, W., Uhmn J., Kim, J.: An improvement technique for simulated annealing and its application to nurse scheduling problem. Int. J. Softw. Eng. Appl. 7(4), (2013)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)
Bai, R., Burke, K.E., Kendall, G., Li, J., McCollum, B.: A hybrid evolutionary approach to the nurse rostering problem, evolutionary computation. IEEE Trans. Evol. Comput. 14(4), 580–590 (2010). ISSN: 1089-778X
Dias, T.M., Ferber, D.F., de Souza, C.C., Moura, A.V.: Constructing nurse schedules at large hospitals. Int. Trans. Oper. Res. 10, 245–265 (2003)
Aickelin, U., Dowsland, K.A.: An indirect genetic algorithm for a nurse-scheduling problem. Comput. Oper. Res. 31(5), 761–778 (2004)
Moz, M., Pato, M.V.: A genetic algorithm approach to a nurse rerostering problem. Comput. Oper. Res. 34, 667–691 (2007). https://doi.org/10.1016/j.cor.2005.03.019
Needleman, J., Buerhaus, P., Mattke, S., Stewart, M., Zelevinsky, K.: Nurse-staffing levels and the quality of care in hospitals. N. Engl. J. Med. 346, 1715–1722 (2002). https://doi.org/10.1056/nejmsa02247
Tsai, C., Li, A.H.S.: A two-stage modeling with genetic algorithms for the nurse scheduling problem. Expert Syst. Appl. 36, 9506–9512 (2009)
Aickelin, U., White, P.: Building better nurse scheduling algorithms. Ann. Oper. Res. 128(1), 159–177 (2004). https://doi.org/10.1023/b:anor.0000019103.31340.a6
Moscato, P., Cotta, C.: A modern introduction to memetic algorithms (Chapter 6). In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, International Series in Operations Research & Management Science, vol. 146, pp. 141–183. Springer, US (2010). https://doi.org/10.1007/978-1-4419-1665-5
Aickelin, U.: An indirect genetic algorithm for set covering problems. J. Oper. Res. Soc. 53(10), 1118–1126 (2002)
Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4), 406–425 (1987)
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Sarkar, P., Sinha, D., Chaki, R. (2018). A Framework for Solution to Nurse Assignment Problem in Health Care with Variable Demand. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 667. Springer, Singapore. https://doi.org/10.1007/978-981-10-8183-5_1
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