A new pairwise comparison based method of ranking LR-fuzzy numbers

Authors:
Mingxin Zhang;Fusheng Yu
Affiliations:
School of Mathematical Sciences, Beijing Normal University, Laboratory of Mathematics and Complex Systems, Ministry of Education, Beijing, China;School of Mathematical Sciences, Beijing Normal University, Laboratory of Mathematics and Complex Systems, Ministry of Education, Beijing, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Year:
2010

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Abstract

This paper aims to rank LR-fuzzy numbers (LR-fns) by the pairwise comparison based method. Different from the existing methods, our method uses the information contained in each LR-fn to get a consistent total order. In detail, since an LR-fn may not be absolutely larger or smaller than another, this paper proposes the concept of dominant degree to quantify how much one LR-fn is larger and smaller than another. From the dominant degrees, we construct a pairwise comparison matrix based on which a consistent ranking is got. Meanwhile, the ranking result is transitive and consistent, and agrees with our intuition. Examples and comparison with existing methods show the good performance of our method.