Article Abstract
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Title: |
Handling constraints for manufacturing process optimisation using genetic algorithms |
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Author: |
Jing Ying Zhang, John B. Morehouse, Steven Y. Liang, Jun Yao, Xiaoqin Zhou 
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Address: |
George W. Woodruff School of Mechanical Engineering, Manufacturing Research Center, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA. ' George W. Woodruff School of Mechanical Engineering, Manufacturing Research Center, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA. ' George W. Woodruff School of Mechanical Engineering, Manufacturing Research Center, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA. ' Shanghai Machine Tool Works, Shanghai 200093, PR China. ' Shanghai Machine Tool Works, Shanghai 200093, PR China |
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Journal: |
International Journal of Computer Applications in Technology 2007 - Vol. 28, No.1 pp. 9 - 19 |
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Abstract: |
Handling constraints is a common challenge to all optimisation methods. To no exception is the planning and optimisation of manufacturing processes that often involves a number of constraints reflecting the complicated reality of manufacturing to which the pursuit of the best operation condition is subject. Mathematical models describing today's manufacturing processes are generally discontinuous, non-explicit, and not analytically differentiable; all of which renders traditional optimisation methods difficult to apply. Genetic Algorithm (GA) is known to provide an optimisation platform method capable of treating highly nonlinear and ill-behaved complex problems, thereby making it an appealing candidate. However, several issues in regard to the handling constraints must be rigorously addressed in order for GA to become a viable and effective method for manufacturing optimisation. In this paper, a new constraint handling strategy combined with (α,μ)-population initialisation is proposed. Twelve numerical test cases and one surface grinding process optimisation are presented to evaluate its optimisation performance. |
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Keywords: |
genetic algorithms; GA; constraint handling; penalty functions; manufacturing processes; process optimisation; population initialisation. |
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DOI: |
10.1504/IJCAT.2007.012326 |
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