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Early stages of a product's design are critical for decisions impacting the entire life-cycle cost. Product designers have mastered the first generation, but they have no ability to know the impact of their decisions on multi-generational products. There is a need for tools that aim at closing the gap between total life-cycle information and the traditional design process. This paper presents a framework for a decision support tool that uses a combination of a life-cycle costing methodology and an evolutionary algorithm to assess design decisions specifically related to material selection. A case study is included to validate the new methodology.
Bradley et al. (Fri,) studied this question.