An introduction of dominant genes in genetic algorithm for FMS

This paper proposes a new idea, namely genetic algorithms with dominant genes (GADG) in order to deal with FMS scheduling problems with alternative production routing. In the traditional genetic algorithm (GA) approach, crossover and mutation rates should be pre-defined. However, different rates app...

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Bibliographic Details
Published in:International journal of production research 2008-08, Vol.46 (16), p.4369-4389
Main Authors: Chan, F. T. S., Chung, S. H., Chan, L. Y.
Format: Article
Language:English
Subjects:
Applied sciences
Dominant genes
Exact sciences and technology
Flexible manufacturing systems
FMS
Genetic algorithms
Inventory control, production control. Distribution
Job shops
Mutation
Operational research and scientific management
Operational research. Management science
Production scheduling
Production scheduling problems
Scheduling, sequencing
Studies
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Summary:This paper proposes a new idea, namely genetic algorithms with dominant genes (GADG) in order to deal with FMS scheduling problems with alternative production routing. In the traditional genetic algorithm (GA) approach, crossover and mutation rates should be pre-defined. However, different rates applied in different problems will directly influence the performance of genetic search. Determination of optimal rates in every run is time-consuming and not practical in reality due to the infinite number of possible combinations. In addition, this crossover rate governs the number of genes to be selected to undergo crossover, and this selection process is totally arbitrary. The selected genes may not represent the potential critical structure of the chromosome. To tackle this problem, GADG is proposed. This approach does not require a defined crossover rate, and the proposed similarity operator eliminates the determination of the mutation rate. This idea helps reduce the computational time remarkably and improve the performance of genetic search. The proposed GADG will identify and record the best genes and structure of each chromosome. A new crossover mechanism is designed to ensure the best genes and structures to undergo crossover. The performance of the proposed GADG is testified by comparing it with other existing methodologies, and the results show that it outperforms other approaches.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207540600632190