This article presents and investigates advanced methodologies for harmonising expert judgments in high-dimensional decision-making problems characterised by uncertainty and imprecise information. The focus is on analysing fuzzy pairwise comparison matrices to evaluate the relative efficiency of technical projects, particularly in complex domains such as space program planning. Several novel techniques are proposed to handle fuzzy preference data. Defuzzification of pairwise comparison matrices. Identification of the median fuzzy pairwise comparison matrix. Computation of fuzzy average values for each matrix element. Derivation of fuzzy weights for evaluated alternatives at discrete levels of membership function values. These approaches aim to preserve the richness of expert uncertainty while enabling structured aggregation. The process of determining final membership functions for project rankings is formalised as an optimisation problem, specifically, the minimisation of deviations between individual experts’ fuzzy pairwise comparison matrices and a synthesised consensus matrix. This framework ensures that the collective judgment remains as close as possible to individual expert inputs while maintaining mathematical rigour.