Solving Complex Nurse Scheduling Problems Using Particle Swarm Optimization
The aim of this paper is to tackle the fairness issue in nurse scheduling problems using the particle swarm optimization approach. In previous studies, there are many factors that influence the nurse scheduling that were listed out including coverage, quality, stability, flexibility, and cost of the schedule, that are very difficult to measure. Today’s health care planning faces more demands and challenges in fulfilling patient-care requirements that will definitely affect the nurse scheduling. In assigning task among nurses, not just the factors named above need to be considered, but fairness needs to be taken into account as well. Definitely, there will be differences between the previous and current nurse scheduling researches. This is especially true when the object of the schedule involves humans. The result from the simulated data represents how the schedule is generated with consideration to several constraints and the fair fitness is measured.
Copyright © 2016 Praise Worthy Prize - All rights reserved.
J. R. Henly and S. J. Lambert, “Schedule Flexibility and Unpredictability in Retail: Implications for Employee Work-Life Outcomes,” no. July, pp. 1–44, 2010.
L. Augustine, M. Faer, A. Kavountzis, and R. Patel, “A Brief Study of the Nurse Scheduling Problem ( NSP ),” 2009.
R. Silvestro and C. Silvestro, “An evaluation of nurse rostering practices in the National Health Service.,” J. Adv. Nurs., vol. 32, no. 3, pp. 525–35, Sep. 2000.
M. N. Azaiez and S. S. Al Sharif, “A 0-1 goal programming model for nurse scheduling,” Comput. Oper. Res., vol. 32, no. 3, pp. 491–507, Mar. 2005.
V. M. Z. Aksin, M. Armony, “The Modern Call Center: A Multi-,” vol. 16, no. 6, pp. 665–688, 2008.
B. Trust, Nurse retention and recruitment: developing a motivated workforce. International Council of Nurses, 2005.
and M. V. B. Maenhout, “Branching Strategies in a Branch-and-Price Approach for a Multiple Objective Nurse Scheduling Problem,” Journal of Scheduling, February 2010, Volume 13, Issue 1, pp 77–93.
V. L. H. Burke E, De Causmaecker P, Vanden Berghe G, “The State Of The Art Of Nurse Rostering,” Journal of Scheduling, November 2004, Volume 7, Issue 6, pp 441–499.
M. Hakimi, A. Ibrahim, and R. Ahmad, “Optimization of Genetic Algorithm for Automated Nurse Schedule,” 2012.
Ramli, M., Hussin, B., Ibrahim, N., Utilizing Particle Swarm Optimisation Techniques in Solving Unfair Nurse Scheduling Problem, (2013) International Review on Computers and Software (IRECOS), 8 (9), pp. 2205-2212.
A. Jan, M. Yamamoto, and A. Ohuchi, “Evolutionary algorithms for nurse scheduling problem,” Proc. 2000 Congr. Evol. Comput. CEC00 (Cat. No.00TH8512), vol. 1, pp. 196–203, 2000.
Tsai, C. C. and C. J. Lee, “Optimization of nurse scheduling problem with a two-stage mathematical programming model,” Asia Pacific Journal of Management, 15, 503–516 (2010).
D. Halfer and E. Graf, “Graduate nurse perceptions of the work experience.,” Nurs. Econ., vol. 24, no. 3, pp. 150–5, 123, 2006.
L. Bailyn, R. Collins, and Y. Song, “Self-scheduling for hospital nurses: an attempt and its difficulties.,” J. Nurs. Manag., vol. 15, no. 1, pp. 72–7, Jan. 2007.
James Bailey, John Field, Personnel scheduling with flexshift models, Journal of Operations Management, Volume 5, Issue 3, 1985, Pages 327-338.
F. He and R. Qu, “A constraint programming based column generation approach to nurse rostering problems,” Comput. Oper. Res., vol. 39, no. 12, pp. 3331–3343, Dec. 2012.
P. & Kumara, “Automated System For Nurse Sheduling Using Graph,” Indian Journal of Computer Science and Engineering, vol. 2, no. 3, pp. 476–485, 2011.
L. Altamirano, M.-C. Riff, and L. Trilling, “A PSO algorithm to solve a real anaesthesiology nurse scheduling problem,” 2010 Int. Conf. Soft Comput. Pattern Recognit., pp. 139–144, Dec. 2010.
F. Neri, E. Mininno, and G. Iacca, “Compact Particle Swarm Optimization,” Inf. Sci. (Ny)., vol. 239, pp. 96–121, Aug. 2013.
J. Li, “A Bayesian Optimization Algorithm for the Nurse Scheduling Problem,” no. Pearl 1998, pp. 2149–2156, 2003.
E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based optimization algorithms,” Adv. Eng. Informatics, vol. 19, no. 1, pp. 43–53, Jan. 2005.
A. R. Güner, “A Continuous And A Discrete Particle Swarm Optimization,” no. June, 2006.
V. Kalivarapu, J.-L. Foo, and E. Winer, “Improving solution characteristics of particle swarm optimization using digital pheromones,” Struct. Multidiscip. Optim., vol. 37, no. 4, pp. 415–427, Mar. 2008.
S. G. Ponnambalam, N. Jawahar, and S. Chandrasekaran, “Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling,” 2009.
S. Consoli, J. A. Moreno-Pérez, K. Darby-Dowman, and N. Mladenović, “Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem,” Nat. Comput., vol. 9, no. 1, pp. 29–46, Jun. 2009.
G. Karimi and A. Lotfi, “An analog/digital pre-distorter using particle swarm optimization for RF power amplifiers,” AEU - Int. J. Electron. Commun., vol. 67, no. 8, pp. 723–728, Aug. 2013.
K. O. Jones, “Comparison Of Genetic Algorithm And Particle Swarm Optimisation,” pp. 1–6, 2005.
N. Todorovic and S. Petrovic, “Bee Colony Optimization Algorithm for Nurse Rostering,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 43, no. 2, pp. 467–473, Mar. 2013.
I. Maruta, T.-H. Kim, D. Song, and T. Sugie, “Synthesis of fixed-structure robust controllers using a constrained particle swarm optimizer with cyclic neighborhood topology,” Expert Syst. Appl., vol. 40, no. 9, pp. 3595–3605, Jul. 2013.
M. K. and J. P. Jiawei Han, Data Mining Concepts and Techniques. 2012.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2022 Praise Worthy Prize