A Ranking Model for Intuitionistic Fuzzy Preference Relation Under Uncertainty for Targeted Poverty

Intuitionistic fuzzy preference relation (IFPR) is a suitable tool to present preference information and hesitation for decision maker among poverty alleviation candidates. This paper aims to provide an effective decision method for helping decision maker accurately to obtain priority weights by using IFPRs to represent the proportion of importance from candidates and rank them. Firstly, a fractional interactive minimizing deviation programming model based on multiplicative consistency-based method to derive weight vectors of alternatives from IFPRs based on multiplicative consistency is presented. Specifically, for any IFPR, by minimizing its absolute deviation from the corresponding consistent IFPR, the weight vectors are generated. Then, a new score function based on Euclidean distance is proposed. This defuzzification value is computed by constructing the idea of closest to ideal solution based on distance measure and ranks alternatives. Subsequently, a numerical example about targeted poverty alleviation selection is given to demonstrate the methods effectiveness.