| System affordability is a growing concern in the design and development of civilian, military, and commercial complex engineered systems. Schedule delays, cost overruns, and performance shortfalls are often default outcomes of such developments. In recent years, research efforts have been focused on exploring the solution space more effectively to find better solutions. However, industry and governmental organizations have not yet been able to apply those techniques to their full potential.;The present dissertation asserts that the size of the solution space relates to the probability of finding affordable solutions. As a result, the effectiveness of tradespace exploration techniques is limited by the size of the solution space. Recognizing that system requirements restrict the solution space, this research creates models to elicit requirements that could facilitate the maximization of the solution space for a given set of stakeholder needs. As a result, the probability of finding more affordable solutions during tradespace-exploration is also maximized.;This research contributes to the body of knowledge of systems engineering and to its state of the art in three areas: systems theory, complexity science, and systems engineering methods. First, a set of definitions, theorems, and corollaries formally prove how stakeholder needs, system requirements, solution spaces, and system affordability are related. Second, the concept of problem complexity and an analytical framework to sum up different types of complexities are developed. Problem complexity measures the lower bound of complexity a system could achieve, given a set of requirements. Third, two methods to reduce such complexity during requirements elicitation are developed. The first method, inspired in Max-Neef's model of human needs, facilitates the identification of constraints that limit the solution space without supporting the satisfaction of new needs. The second method, based on the concept of elementary decomposition, facilitates the identification of conflicting requirements and enables challenging decisions at higher levels of the architecture.;Research hypotheses are validated by a combination of mathematical proof, case studies, and field tests. The results of the present research are generalized to discrete requirements, fuzzy requirements, and continuous requirements or value functions. |
