UTA methods concludes to the assessment of additive value preference models based on Decision Makers (DMs) global preferences expressed by rank ordering of a limited set of the alternative actions evaluated into a consistent family of criteria. An adaptation of UTA methods is presented in this research work which utilizes additional information concerning the global preferences of the DM. The strengths of preferences among the successive alternatives of the reference set, sorted by their ranking, are derived. The DM is asked to provide ranges of values [zmin, zmax], through visual techniques, expressing the degree he/she prefers an alternative action from the next one in the ranking. By this way n-1 ranges of indices concerning the strength of preferences among the successive alternatives of the n alternative actions of the reference set are embedded into the UTA methods. Adaptations of the linear program of UTA method leads to the estimation of a more robust additive value model corresponding to more rich representation of DM's preferences. The proposed adaptation of UTA methods can be used a priori during the initial expression of DM's global preferences or a posteriori in the frame of post optimality and robustness analysis, constituting a new feedback of UTA methods. The above approach is presented and evaluated through an illustration case study.