Modified GA-based optimizer for multi-objective product family design

Product family design has been recognized as an effective method to satisfy diverse customer´s demands cost-effectively. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss compared to individual design. In this paper, a modified genetic algorithm using dynamic weighted aggregation is proposed to optimize a scale-based product family design while making the two-objective (performance-and-commonality) optimization tractable and efficient. The proposed method not only overcomes the drawbacks of conventionally fixed weight aggregation for product family design, but also maintains the computation expense at the economical level. An example of designing a family of planetary gear trains is presented to demonstrate the proposed method.