@inproceedings {pub2588,
	title = {A Cascaded Evolutionary Multi-objective Optimization for Solving the Unbiased Universal Electric Motor Family Problem
},
	author = {Timo Friedrich AND Stefan Menzel},
	year = {2014},
	month = {July},
	abstract = {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 one or more common platform(s) which cover the commonalities of product variants and the derived product variants. While product variants need to be fast and finely adjusted to market needs, from manufacturing point of view a minimal set of underlying product platforms is required to increase cost efficiency.
For the design and evaluation of optimization methods in the present paper the universal electric motor (UEM) family problem is considered since it provides a fair trade-off between complexity and computationally costs compared to real world application scenarios in the automotive domain. Solving this problem without usage of pre-knowledge comes with high computational costs. A cascaded evolutionary multi-objective optimization based on NSGA2 with concatenation of resulting Pareto fronts is proposed in this paper to efficiently reduce computational time. Besides providing the Pareto solutions to the unbiased UEM family problem the advantage of pre-initialization for changing product specifications is shown.
},
	publisher = {IEEE},
	booktitle = {2014 IEEE Congress on Evolutionary Computation (IEEE CEC)},
	address = {Beijing, China (to appear)}
}