
	Journal of Applied Sciences
	Year: 2006 \| Volume: 6 \| Issue: 4 \| Page No.: 775-778 DOI: 10.3923/jas.2006.775.778

Sequential and Parallel Genetic Algorithms for the Hybrid Flow Shop Scheduling Problem

K. Belkadi, M. Gourgand , M. Benyettou and A. Aribi

Abstract: In this study we treat the scheduling problem in Hybrid Flow Shop production systems (HFS) and that using the sequential Genetic Algorithms (GA) and Massively Parallel Genetic Algorithms (MPGA). A comparison between the sequential and parallel version of Genetic Algorithm is established. This comparison relates to the quality of the solution. The simulation of parallelization according to the massively parallel model by using a MIMD architecture could improve considerably the quality of the solutions obtained, compared with sequential GA. This success of this model of parallelism is closely linked to its parameters and in particular the number of threads implied in the search and the replacement strategy of the individuals. The replacement of the individuals by application of the temperature principle of simulated annealing could give results clearly better than the MPGA with competition.

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How to cite this article:

K. Belkadi, M. Gourgand , M. Benyettou and A. Aribi , 2006. Sequential and Parallel Genetic Algorithms for the Hybrid Flow Shop Scheduling Problem. Journal of Applied Sciences, 6: 775-778.

DOI: 10.3923/jas.2006.775.778

URL: https://scialert.net/abstract/?doi=jas.2006.775.778

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