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Comprehensive machine cell/part family formation using genetic algorithms

Comprehensive machine cell/part family formation using genetic algorithms 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Manufacturing Technology Management Emerald Publishing

Comprehensive machine cell/part family formation using genetic algorithms

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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
1741-038X
DOI
10.1108/17410380410547843
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Manufacturing Technology ManagementEmerald Publishing

Published: Sep 1, 2004

Keywords: Programming and algorithm theory; Parts; Process efficiency

References

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