The research objective of this award is to develop an optimization-based design methodology that accounts for the dynamic nature of complex systems. The methodology will have a modeling capability to conceptually integrate object decomposition (by physical subsystems) with aspect decomposition (by disciplines) into a network of nodes at which local optimizations are performed subject to the performance targets from the other nodes. The overall network optimization will be coordinated to accomplish a desired compromise between the performance of the design and the performance of the subsystems and disciplines. The dual-decomposition approach will make the methodology comprehensive and responsive to changing design conditions while an agent-based implementation will guarantee computational flexibility. The methodology will incorporate human decision-making in the process and will be the basis for a tool implemented on a layout design problem.
If successful, designers will be able to make decisions for complex systems (such as vehicles or airplanes), while accounting for failures and opportunities resulting from the consideration of the entire spectrum of changing conditions. The outcomes of this research will be disseminated through various channels including graduate student training in engineering and mathematical sciences, interdisciplinary seminars, professional meetings, publications, open source software, and Internet websites. The methodology will be presented to the Army (TACOM), other federal institutions (Air Force, DOE, NASA, Navy), and original equipment manufacturer companies (BMW, Ford, GM, Boeing). Graduate students will be involved in parts of the proposed research while results of this work will be included in graduate-level courses. The proposed work will provide opportunities to train students who can enter the workforce with advanced mathematical skills and an understanding of how to apply these to complex engineering problems. Special efforts will be undertaken to reach out to students from local-area schools that have a significant proportion of underrepresented populations.
