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The product family design problem solved by evolutionary algorithms is discussed.A successful product family design method should achieve an optimal tradeoff among a set of compet- ing objectives,which involves maximizing commonality across the family of products and optimizing the performances of each product in the family.A 2-level chromosome structured genetic algorithm (2LCGA)is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods.By interpreting the chromosome as a 2-level linear structure,the variable commonality genetic algorithm(GA)is constructed to vary the amount of plat- form commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process.By incorporating a commonality assessing index to the problem formulation,the 2LCGA optimize the product platform and its corresponding family of products in a single stage,which can yield improvements in the overall performance of the product family compared with two-stage approaches(the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage). The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization,offering opportunities for supe- rior overall design by more efficacious tradeoffs between commonality and performance.The effec- tiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.
The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competitors, which involves maximizing commonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm (2LCGA) is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm (GA) is constructed to vary the amount of plat- form commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. BY incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches (the first stage templates determining the best settings for the platform variables and values of unique variables are found for each product in the second stage). The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization, offering opportunities for supe- rior overall design by more efficacious tradeoffs between commonality and performance. The effec tiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.