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

Authors:
J. Rezaeian;N. Javadian;R. Tavakkoli-Moghaddam;F. Jolai
Affiliations:
Department of Industrial Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran1;Department of Industrial Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran1;Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran2;Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran2
Venue:
Applied Soft Computing
Year:
2011

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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 nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid 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 genetic algorithm and simulated annealing in generating optimal solutions.