2017 SE Doctoral Dissertation Showcase – Exemplary Recognition
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 trade space 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 trade space 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 elemental 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.
Alejandro Salado Diez is a systems engineering researcher, educator, and consultant. He is an assistant professor of systems engineering with the Grado Department of Industrial & Systems Engineering at Virginia Tech. His research focuses on unveiling the scientific foundations of systems engineering and using them to improve systems engineering practice. He is pioneering research in the area of verification and validation. His approach in this endeavor is transdisciplinary and intersects mathematical foundations, decision analysis and methods, and behavioral and cognitive models. His educational efforts target developing disruptive educational approaches to educate the next generation of systems engineers. Before joining academia, Alejandro spent over ten years as a systems engineer in the space industry, developing and leading space systems of up to $1b. He has published over 40 scientific publications and has received several paper awards. He is a recipient of the Fabrycky-Blanchard Award for Systems Engineering Research and the Fulbright International Science and Technology Award. Dr. Salado holds a BS/MS in electrical engineering from Polytechnic University of Valencia, an MS in project management and a MS in electronics engineering from Polytechnic University of Catalonia, the SpaceTech MEng in space systems engineering from Delft University of Technology, and a PhD in systems engineering from the Stevens Institute of Technology. He is a member of INCOSE and a senior member of IEEE and IISE.
Alejandro Salado Diez, Assistant Professor of Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
Phone: +1 540 552 6656