A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system
作者： Javad Rezaeian Zeidi , Nikbakhsh Javadian , Reza Tavakkoli-Moghaddam , Fariborz Jolai
DOI: 10.1016/J.CIE.2013.08.015
关键词: Job shop 、 Multi-objective optimization 、 Mathematical optimization 、 Sorting 、 Artificial neural network 、 Genetic algorithm 、 Engineering 、 Cellular manufacturing 、 Population-based incremental learning 、 Nonlinear programming
摘要: One important issue related to the implementation of cellular manufacturing systems (CMSs) is decide whether convert an existing job shop into a CMS comprehensively in single run, or stages incrementally by forming cells one after other, taking advantage experiences implementation. This paper presents new multi-objective nonlinear programming model dynamic environment. Furthermore, novel hybrid approach based on genetic algorithm and artificial neural network proposed solve presented model. From computational analyses, found much more efficient than fast non-dominated sorting (NSGA-II) generating Pareto optimal fronts.
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