A cascaded evolutionary multi-objective optimization for solving the unbiased universal electric motor family problem

For a successful business model the efficient development and design of a comprehensive product family plays a crucial part in many real world applications. A product family as it occurs, e.g., in the automotive domain consists of a product platform which covers the commonalities of product variants and the derived product variants. While product variants need to be fast and flexibly adjusted to market needs, from manufacturing and development point of view an underlying product platform with a large number of common parts is required to increase cost efficiency. For the design and evaluation of optimization methods for product family development, in the present paper the universal electric motor (UEM) family problem is considered, as it provides a fair trade-off between complexity and computational costs compared to real world application scenarios in the automotive domain. Since especially solving this problem without usage of pre-knowledge comes with high computational costs, a cascaded evolutionary multi-objective optimization based on NSGA-II with concatenation of product Pareto fronts is proposed in the present paper to efficiently reduce computational time. Besides providing sets of Pareto solutions to the unbiased UEM family problem the effects of considering solutions of prior platform optimizations as starting point for follow-up optimizations under changing requirements are evaluated.