Performance comparison between a classic particle swarm optimization and a genetic algorithm in manufacturing cell design
Autores
Rodríguez León, Johanna
Quiroga Méndez, Jabid Eduardo
Ortiz Pimiento, Nestor Raúl
Director
Tipo de contenido
Artículo de revista
Idioma del documento
EspañolFecha de publicación
2013
Título de la revista
ISSN de la revista
Título del volumen
Documentos PDF
Resumen
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.