Multi-objective design optimization for product platform and product family design using genetic algorithms

Many companies are using product families and platform-based product development to reduce costs and time-to-market while increasing product variety and customization. Multiobjective optimization is increasingly becoming a powerful tool to support product platform and product family design. In this paper, a genetic algorithm-based optimization method for product family design is suggested, and its application is demonstrated using a family of universal electric motors. Using an appropriate representation for the design variables and by adopting a suitable formulation for the genetic algorithm, a one-stage approach for product family design can be realized that requires no a priori platform decision-making, eliminating the need for higher-level problem-specific domain knowledge. Optimizing product platforms using multi-objective algorithms gives the designer a Pareto solution set, which can be used to make better decisions based on the trade-offs present across different objectives. Two Non-Dominated Sorting Genetic Algorithms, namely, NSGA-II and epsilon-NSGA-II, are described, and their performance is compared. Implementation challenges associated with the use of these algorithms are also discussed. Comparison of the results with existing benchmark designs suggests that the proposed multi-objective genetic algorithms perform better than conventional single-objective optimization techniques, while providing designers with more information to support decision making during product family design.