Using multiple genetic operators to reduce premature convergence in genetic assembly planning

Recent research reports describe efforts to create automated assembly sequence planners, which will help design and manufacturing engineers analyze increasingly complex and customized products and the dynamic conditions found in modern computer integrated manufacturing (CIM) systems.Early assembly planners that use graph searching techniques can generally find global-optimal assembly plans for a product, but tend to have long run-times. More recent assembly planners that use genetic algorithms can generally find high-quality solutions quickly, but tend to converge prematurely at local-optimal solutions. The author introduces two new genetic operators to help reduce premature convergence in genetic assembly planners.The results bring automated assembly planning and fully flexible computer integrated manufacturing systems closer to real-world application.

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