Artigos
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/219
Artigos produzidos de convênios e parcerias com IES
Navegar
3 resultados
Resultados da Pesquisa
Item Metodología propuesta para la optimización multicriterio mediante algoritmos genéticos en las aplicaciones industriales(INSTITUTO DE TECNOLOGIA, 2014) CASTRO, Andy Oliveira; BEZERRA, Ulysses Henrique; LEITE, Jandecy Cabral; AZEVEDO, Marcelo da Silva Sousa; José Antônio da Silva SouzaThis paper proposes a new methodology for multi-criteria optimization in modular chip mounters using the NSGA-II genetic algorithm. Fitness functions were modeled, and a multi-criteria optimization tool was created based on machine functions and constraints. The same approach can be applied to other types of machines to support decision-making in industrial environments, aiming at efficiency and cost reduction.Item Optimización del proceso de montaje de componentes SMD usando algoritmo genético multiobjetivo en la industria(INSTITUTO DE TECNOLOGIA, 2014) CIRINO, Weverson dos Santos; MUSTAFE, Nadime; REIS, Ana; CASTRO, Anderson; BEZERRA, Ubiratan Holanda; Professora Dra. Fernanda Souza do NascimentoThe article proposes the optimization of the assembly sequence of SMD components on printed circuit boards using a multi-objective genetic algorithm (NSGA-II). The study was conducted in an electronics industry at the Manaus Industrial Hub, showing that the proposed algorithm outperformed the traditional machine inserter optimizer, reducing assembly time by 6 seconds per board and increasing productivity by 30%. The results highlight the effectiveness of NSGA-II in solving complex industrial optimization problems.Item Optimización Multiobjetivo de Filtros Pasivos(INSTITUTO DE TECNOLOGIA, 2012) LEITE, Jandecy Cabral; ABRIL, Ignacio Pérez; TOSTES, Maria Emilia de Lima; OLIVEIRA, Roberto Celio Limão de; MAGALHÃES, Edilson MarquesDifferent approaches have been used to formulate the problem of passive filter design, classified into single or multi-objective formulations. While previous contributions solve the multi-objective problem by minimizing a single objective function composed of a weighted sum of sub-objectives, this work uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to address the problem. The developed program determines the number, location, configuration, and parameters of passive filters required to obtain non-dominated optimal solutions, considering four predefined filter types. The solution of a practical example demonstrates the effectiveness of the proposed procedure.