System evolvability is vital to the longevity of large-scale complex engineered systems. The need for evolvability in complex systems is a result of their long service lives, rapid advances to their integrated technologies, unforeseen operating conditions, and emerging system requirements. In recent years, quantifiable metrics have been introduced for measuring the evolvability of complex systems based on the amount of excess capability in the system. These metrics have opened opportunities for optimization of systems with evolvability as an objective. However, there are several aspects of such an optimization that require further consideration. For example, there is a trade-off between the cost of excess capability initially built into complex systems and the benefit that is added to the system for future evolution. This trade-off must be represented in the optimization problem formulation. Additionally, uncertainty in future requirements and parameters of complex systems can result in an inaccurate representation of the design space. This thesis addresses these considerations through multi-objective optimization and uncertainty analysis. The resulting analysis gives insight into the effects of designing for evolvability. We show that there is a limit to the value added by increasing evolvability. We also show that accounting for uncertainty changes the optimal amount of evolvability that should be designed into a system. The developed theories and methods are demonstrated on the design of a military ground vehicle.
College and Department
Ira A. Fulton College of Engineering and Technology; Mechanical Engineering
BYU ScholarsArchive Citation
Watson, Jason Daniel, "A Multi-Objective Optimization Method for Maximizing the Value of System Evolvability Under Uncertainty" (2015). All Theses and Dissertations. 5598.
evolvability, reconfigurability, flexibility, adaptability, optimization, multi-objective optimization, uncertainty, aleatory, epistemic, complex systems