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Comprehensive machine cell/part family formation using genetic algorithms

Saeed Zolfaghari (Assistant Professor, Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada)
Ming Liang (Professor, Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 1 September 2004



The solution quality of a comprehensive machine/part grouping problem, where the processing times, lot sizes and machine capacities are considered, may not be properly evaluated using a binary performance measure. This paper suggests a generalized grouping efficacy index which has been compared favorably with two binary performance measures. A genetic algorithm using the generalized performance measure as the objective is developed to solve the comprehensive grouping problems. The algorithm has been tested using a number of reference problems with processing times being randomly assigned to all operations. The effects of three major genetic parameters (population size, mutation rate and the number of crossover points) have also been examined. The results indicate that, when the computational time is fixed, larger population size and lower mutation rate tend to improve solution quality while the number of crossover points has no significant impact on the final solution.



Zolfaghari, S. and Liang, M. (2004), "Comprehensive machine cell/part family formation using genetic algorithms", Journal of Manufacturing Technology Management, Vol. 15 No. 6, pp. 433-444.



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Copyright © 2004, Emerald Group Publishing Limited

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