Comparison of Probabilistic and Fuzzy Multi-Attribute Decision Making Methods for Capturing Uncertainty in Concept Selection

摘要

An evolving set of modern conceptual design methods seek to explore the feasibility of a new generation of systems, with new capabilities, to accomplish missions that conventional vehicles cannot be empirically redesigned to perform. These methods attempt to provide a more complete understanding of a concept's design space, and can accurately forecast a design's feasibility in the face of huge uncertainties at the conceptual stage. Modern Multi-Attribute Decision Making (MADM) techniques are evolving to help designers narrow large sets of potential concept architectures while accounting for the uncertainty inherent in early requirements and pre-modeling, expert based assessment. Here, several under-utilized fuzzy MADM methods are introduced and compared to their more recently proposed and utilized probabilistic counterparts. Probabilistic and fuzzy versions of the popular Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are explored, and their results are applied to the selection of a conceptual system architecture for DARPA's VTOL X-Plane program. Various means for visualizing and comparing the resulting ranks and their related uncertainty are discussed.