Gaussian AHP and Pareto Front obtained through Multi- Attribute Tradespace Exploration (MATE)

The MATE method – Multi-Attribute Tradespace Exploration – was proposed by Adam Ross and Nathan Diller while working at NASA. It is based on the Multi Attribute Utility Theory – MAUT, developed by Ralph Keeney and Howard Raiffa in 1976 and used for eliciting requirements and defining utility functions for a great number of design alternatives, as well as for positioning those alternatives relative to a bidimensional Pareto front. On the other hand, the Gaussian Analytic Hierarchy Process – Gaussian AHP – is an evolution proposed by Dos Santos et al. in 2021 for the classic AHP Method, which eliminates the need of pair comparison of attributes for each design alternative and introduces the relationship between standard deviations and mean scores in order to increase the reliability of the generated ranking. In this article the authors use a case study proposed by Adam Ross to confront the Pareto front generated by MATE method with the ranking generated by the Gaussian AHP method and propose a modification of that method.