Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A genetic algorithm approach to the machine-component grouping problem with multiple objectives
Computers and Industrial Engineering
Computers and Industrial Engineering
Design and Analysis of Experiments
Design and Analysis of Experiments
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Manufacturing cell formation with production data using neural networks
Computers and Industrial Engineering
Estimation of Distribution Algorithms for the Machine-Part Cell Formation
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
An ant colony optimization metaheuristic for machine-part cell formation problems
Computers and Operations Research
Computers and Industrial Engineering
A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain
Computers and Industrial Engineering
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This paper reports a new genetic algorithm (GA) for solving a general machine/part grouping (GMPG) problem. In the GMPG problem, processing times, lot sizes and machine capacities are all explicitly considered. To evaluate the solution quality of this type of grouping problems, a generalized grouping efficacy index is used as the performance measure and fitness function of the proposed genetic algorithm. The algorithm has been applied to solving several well-cited problems with randomly assigned processing times to all the operations. To examine the effects of the four major factors, namely parent selection, population size, mutation rate, and crossover points, a large grouping problem with 50 machines and 150 parts has been generated. A multi-factor (34) experimental analysis has been earned out based on 324 GA solutions. The multi-factor ANOVA test results clearly indicate that all the four factors have a significant effect on the grouping output. It is also shown that the interactions between most of the four factors are significant and hence their cross effects on the solution should be also considered in solving GMPG problems.