Optimizing Automated Manufacturing Processes Using a Hybrid BRKGA Algorithm: A Case Study on Flexible Job-Shop Scheduling
Por:
Lopez-Juarez I., Vazquez-Lopez J.A., Osorio-Comparan R., Puente-Lira E.E., Hernandez-Lopez A., Lefranc G.
Publicada:
1 ene 2024
Resumen:
In the modern era, industrial automation offers significant competitive advantages. Replacing human labor with robots allows processes to be carried out much faster and more efficiently, with lower waste rates and the possibility of scheduling production 24/7. However, this also presents new challenges and opportunities in optimizing these automated processes. This paper proposes a hybrid algorithm BRKGA (Biased Random Key Genetic Algorithm) to optimize flexible job shop scheduling (FJSSP) problems in automated manufacturing systems. The algorithm is applied to a real-world case study in the Cinvestav Intelligent Manufacturing Laboratory, seeking to optimize the production of a four-piece product. The results demonstrate that the proposed algorithm outperforms previous results in terms of both, solution quality and computational efficiency. © 2024 The Authors.
Filiaciones:
Lopez-Juarez I.:
CINVESTAV, Ind. Metalurgica 1062, P. Ind. Saltillo-Ramos Arizpe, Coah., Ramos Arizpe, 25900, Mexico
Universidad Autónoma de Yucatan, Industrias No Contaminantes S/N Periferico Norte, Yuc., Mérida, 97302, Mexico
Vazquez-Lopez J.A.:
Tecnologico Nacional de Mexico (TecNM) Campus Celaya, Antonio García Cubas Pte 600, Gto, Celaya, Mexico
Osorio-Comparan R.:
IIMAS-UNAM, Ciudad Universitaria, Circuito Escolar S/N, Alcaldía Coyoacán CDMX, 04510, Mexico
Puente-Lira E.E.:
CINVESTAV, Ind. Metalurgica 1062, P. Ind. Saltillo-Ramos Arizpe, Coah., Ramos Arizpe, 25900, Mexico
Hernandez-Lopez A.:
Sistema Avanzado de Bachillerato y Educacion Superior, Celaya, 38010, Mexico
Lefranc G.:
Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2950, Valparaíso, 2430000, Chile
All Open Access; Gold Open Access
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