Product environmental impact reduction efforts largely focus on incremental changes during detailed design. Application of automated concept generation using a design repository and integral life cycle assessment approach is explored to evaluate and reduce environmental impacts in the conceptual phase of product design.
Sustainability requires emphasizing the importance of environmental causes and effects among design knowledge from heterogeneous stakeholders to make a sustainable decision. Recently, such causes and effects have been well developed in ontological representation, which has been challenged to generate and integrate multiple domain knowledge due to its domain specific characteristics. Moreover, it is too challengeable to represent heterogeneous, domain-specific design knowledge in a standardized way. Causal knowledge can meet the necessity of knowledge integration in domains. Therefore, this paper aims to develop a causal knowledge integration system with the authors’ previous mathematical causal knowledge representation.
Related to the recent issues on the environmental sustainability, the attention and importance of Reusable Medical Equipment (RME) has increased rapidly. As a part of System Redesign Project funded by Veterans Engineering Resource Center (VERC), “Design Evaluation for Reusable Medical Equipment” project has been conducted. This research project aims to develop new RME design assessment and evaluation framework and Design for Reusability (DFR) and Design for Sustainability (DFS) principles. In this paper, we will present a decision support system for RME design evaluation, based on DFR and DFS principles. To illustrate the proposed new framework, GI endoscope is used in this research. In the proposed system, we apply a Rough Set Theory to identify the relationships among design and reprocessing features. Also we use feature selection technique to select the customized features from the design features and reprocessing features to be used for design evaluation.
A method is introduced to incorporate sustainability considerations in the early design stages, while simultaneously accounting for supply chain factors, such as cost and lead time. Overall, this work is our first step in understanding the trade-offs between sustainability metrics and more traditional supply chain performance metrics (i.e., cost and lead time). Based on our understanding of these trade-offs, we intend to help build computational artificial intelligence tools that can exploit these trade-offs for improved customization in produc