Determining Feasibility Resilience: Set Based Design Iteration Evaluation Through Permutation Stability Analysis

The goal of robust design is to select a design that will still perform satisfactorily even with unexpected variation in design parameters. A resilient design will accommodate unanticipated future system requirements. Through studying the variations of system parameters through the use of multi objective optimization, a designer hopes to locate a robustly resilient design, which performs current mission well even with varying system parameters and is able to be easily repurposed to new missions. This ability to withstand changes is critical because it is common for the product of a design to undergo changes throughout its life cycle. This subject has been an active area of research in industrial design and systems engineering but most methodologies rest upon exhaustive understanding of design, manufacturing and mission variance. The thrust of this research is to develop new methodologies for estimating robust resilience given imperfect information. In this work we will apply new methodologies for locating resilient designs within a dataset derive from a study performed by the Small Surface Combatant Task Force in order to improve upon a state of the art design process. Two new methodologies, permutation stability analysis and mutation stability analysis, are presented along with results and discussion as applied to the SSCTF dataset. It is demonstrated that these new methods improve upon the state of the art by providing insight into the robustness and resilience of selected system properties. These methodologies, although applied to the SSCTF dataset are posed more generally for wider application in system design

Robustness, Optimization, and Architectures

This paper will review recent progress on developing a unified theory for complex networks from biological systems and physics to engineering and technology. Insights into what the potential universal laws, architecture, and organizational principles are can be drawn from three converging research themes: growing attention to complexity and robustness in systems biology, layering and organization in network technology, and new mathematical frameworks for the study of complex networks. We will illustrate how tools in robust control theory and optimization can be integrated towards such unified theory by focusing on their applications in biology, physics, network design, and electric grid