A Design Space Exploration Methodology to Support Decisions under Evolving Requirements' Uncertainty and its Application to Suborbital Vehicles

摘要

This research aims at supporting the development of emerging markets such as suborbital vehicles by establishing a methodology that enables a broad design space exploration at a conceptual level to select the best concepts against unclear objectives and under evolving requirements' uncertainty. To bridge the gap in current design space exploration techniques, a new architecture-based morphological matrix is developed to generate all feasible concepts. Then, a new evolutionary algorithm based on architecture fitness is implemented that drives multi-objective optimization algorithms to simultaneously compare and optimize all configurations. To support decisions under evolving uncertainty, requirements are modeled by membership functions and are propagated using fuzzy set theory. The new methodology is expected to reduce the risk of missing promising concepts and help designers with challenging go/no-go decisions. It will also provide more flexibility by allowing decision makers to develop scenarios and support more analytic decisions.