A predictive product attribute driven eco-design process using depth-first search

Abstract

Life cycle assessment (LCA) for new designs is a difficult and time-consuming task. This study creates a predictive eco-design process using depth-first search. The approach uses a multi-attribute function, a similarity threshold, and depth-first search (DFS) to match new designs to previous designs, search the similarity graph, separate designs into groups, and predicts environmental impacts for new designs from previous designs. The product attributes in this study are primarily product modules or product components that have major effects on the environmental impacts. Since there is always some uncertainty when estimating LCA results, the minimum and maximum LCA results from previous designs in each group are used to predict environmental impacts for new designs, as ranges. Case study results show that the method can be used to improve product design, early in the eco-design process. Consequently, the process can be used to reduce design time and reduce design cost.