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

•We present a new mathematical model for incremental cell formation problem.•We design an intelligence system based on neural network to cluster the solution area.•We develop a hybrid GA and ANN to solve incremental cell formation problem.•The efficiency of the proposed algorithm tested on many test...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 66; no. 4; pp. 1004 - 1014
Main Authors: Zeidi, Javad Rezaeian, Javadian, Nikbakhsh, Tavakkoli-Moghaddam, Reza, Jolai, Fariborz
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.12.2013
Pergamon Press Inc
Subjects:
Genetic algorithm
Genetic algorithms
Incremental cell formation
Manufacturing cells
Multi-objective optimization
Neural network
Neural networks
Nonlinear programming
Pareto optimum
Studies
Multi-objective optimization
Neural network
Genetic algorithm
Incremental cell formation
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•We present a new mathematical model for incremental cell formation problem.•We design an intelligence system based on neural network to cluster the solution area.•We develop a hybrid GA and ANN to solve incremental cell formation problem.•The efficiency of the proposed algorithm tested on many test problems. 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.
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ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2013.08.015