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.
Stehr, Mark-Oliver, Avar, Peter, Korte, Andrew R., Parvin, Lida, Sahab, Ziad J., Bunin, Deborah I., Knapp, Merrill, Nishita, Denise, Poggio, Andrew, Talcott, Carolyn L., Davis, Brian M., Morton, Christine A., Sevinsky, Christopher J., Zavodszky, Maria I., Vertes, Akos
There is an abundance of complex dynamic systems that are critical to our daily lives and our society but that are hardly understood, and even with today's possibilities to sense and collect large amounts of experimental data, they are so complex and continuously evolving that it is unlikely that their dynamics will ever be understood in full detail. Nevertheless, through computational tools we can try to make the best possible use of the current technologies and available data. We believe that the most useful models will have to take into account the imbalance between system complexity and available data in the context of limited knowledge or multiple hypotheses. The complex system of biological cells is a prime example of such a system that is studied in systems biology and has motivated the methods presented in this paper. They were developed as part of the DARPA Rapid Threat Assessment (RTA) program, which is concerned with understanding of the mechanism of action (MoA) of toxins or drugs affecting human cells. Using a combination of Gaussian processes and abstract network modeling, we present three fundamentally different machine-learning-based approaches to learn causal relations and synthesize causal networks from high-dimensional time series data. While other types of data are available and have been analyzed and integrated in our RTA work, we focus on transcriptomics (that is gene expression) data obtained from high-throughput microarray experiments in this paper to illustrate capabilities and limitations of our algorithms. Our algorithms make different but overall relatively few biological assumptions, so that they are applicable to other types of biological data and potentially even to other complex systems that exhibit high dimensionality but are not of biological nature.
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