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

  • Dongxu Yu
  • Sidong XianEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


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.


Intuitionistic fuzzy preference relation (IFPR) Priority weights New score function Targeted poverty alleviation 



The authors express their gratitude to the anonymous Reviewers for their valuable and constructive comments. And this work was supported by the Chongqing Social Science Planning Project (No. 2018YBSH085), Graduate Teaching Reform Research Program of Chongqing Municipal Education Commission (No. YJG183074), Major entrustment projects of the Chongqing Bureau of quality and technology supervision (No. CQZJZD2018001), Chongqing research and innovation project of graduate students (No. CYS18252, No. CYS17227) and the Science and Technology Research Project of Chongqing Municipal Education Commission(Grant number: KJQN201800624).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of AutomationChongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China
  2. 2.Key Laboratory of Intelligent Analysis and Decision on Complex SystemsChongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China

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