Title :
Methodology proposal for multicriteria optimization using NSGA-II in industrial applications
Author :
Castro, A.O. ; Bezerra, U.H. ; Leite, J.C. ; Azevedo, M.S.S.
Author_Institution :
Post Graduation Program in Electr. Eng. - PPGEE, Fed. Univ. of Para - UFPA, Belem, Brazil
Abstract :
The optimization problems of industrial process attracted many researches since the early 90´s of the last century. The production volume increase, lifespan shorten of the products and technological advances pushed the industries to seek for low cost and quick implementation solutions. One of the processes that became the core for increasing the sales volumes was the Surface Mount Technology - SMT composed by printing, automated chip mounting and reflow which replaced the Through Hole Technology - THT. This paper formulated a new point of view for modular chip mounters based on the already known feeder assignment problem and head motion problem applying the global optimization using the nonsorting dominance genetic algorithm the NSGAH in regard of the total cycle time reduction. The modeling of the fitness functions were presented and the multi criteria optimization tool was described using the machine functions and constraints. The same method could be applied to describe other type of machines to support future research.
Keywords :
genetic algorithms; operations research; printing; product life cycle management; production management; surface mount technology; NSGA-II; SMT; THT; automated chip mounting; feeder assignment problem; fitness function modeling; global optimization; head motion problem; industrial applications; industrial process; machine constraints; machine functions; modular chip mounters; multicriteria optimization problem; multicriteria optimization tool; nonsorting dominance genetic algorithm; printing; product lifespan; production volume; surface mount technology; through hole technology; total cycle time reduction; Genetic algorithms; Head; Linear programming; Magnetic heads; Optimization; Sociology; Statistics; chip mounter; multicriteria optimization; nonsorting genetic algorithm; objective function;
Conference_Titel :
Industry Applications (INDUSCON), 2014 11th IEEE/IAS International Conference on
Print_ISBN :
978-1-4799-5550-3
DOI :
10.1109/INDUSCON.2014.7059411