Performance comparison between a classic particle swarm optimization and a genetic algorithm in manufacturing cell design

This article studies the performance of two metaheuristics, the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), in the manufacturing cell formation problem of a factory that needs to organize three production cases in an efficient way for four, five and six manufacturing cells to p...

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Autores:: Rodríguez León, Johanna
Quiroga Méndez, Jabid Eduardo
Ortiz Pimiento, Nestor Raúl

Tipo de recurso:: Article of journal

Fecha de publicación:: 2013

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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Summary:	This article studies the performance of two metaheuristics, the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), in the manufacturing cell formation problem of a factory that needs to organize three production cases in an efficient way for four, five and six manufacturing cells to produce 30, 40 and 50 different products to be processed in 10, 10 and 20 type machines, respectively. The procedure for adjusting the particular parameters of each algorithm is implemented through a Design of Experiments which includes their own analysis of variance. Both algorithms are implemented in Matlab®. The results obtained by each meta heuristic are compared in terms of the cost of the best solution found and the execution time used to find that solution, so that it is possible to establish which methodology is the most appropriate when solving this optimization problem.

Performance comparison between a classic particle swarm optimization and a genetic algorithm in manufacturing cell design

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