A modified genetic algorithm for product family optimization with platform specified by information theoretical approach

Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approachesare single-platform methods, in which design variables are either shared across all product variants or not atall. While in multiple-platform design, platform variables can have special value with regard to a subset ofproduct variants within the product family, and offer opportunities for superior overall design. An informationtheoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variablesselection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposedand developed for optimizing the corresponding product family in a single stage, simultaneously determiningthe optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which thefirst stage involves determining the best settings for the platform and values of unique variables are found foreach product in the second stage. An example of design of a family of universal motors was used to verify theproposed method.