A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

Authors:
Javad Rezaeian Zeidi;Nikbakhsh Javadian;Reza Tavakkoli-Moghaddam;Fariborz Jolai
Affiliations:
Department of Industrial Engineering, Faculty of Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran;Department of Industrial Engineering, Faculty of Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran;Department of Industrial Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365-4563, Tehran, Iran;Department of Industrial Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365-4563, Tehran, Iran
Venue:
Computers and Industrial Engineering
Year:
2013

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Abstract

One important issue related to the implementation of cellular manufacturing systems (CMSs) is to decide whether to convert an existing job shop into a CMS comprehensively in a single run, or in stages incrementally by forming cells one after the other, taking the advantage of the experiences of implementation. This paper presents a new multi-objective nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid multi-objective approach based on the genetic algorithm and artificial neural network is proposed to solve the presented model. From the computational analyses, the proposed algorithm is found much more efficient than the fast non-dominated sorting genetic algorithm (NSGA-II) in generating Pareto optimal fronts.