Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi attribute group decision making
Heronian mean (HM) operator has the advantages of considering the interrelationships between parame-ters, and linguistic intuitionistic fuzzy number (LIFN), in which the membership and non-membership areexpressed by linguistic terms, can more easily describe the uncertain and the vague information existingin the real world. In this paper, we propose the partitioned Heronian mean (PHM) operator which assumesthat all attributes are partitioned into several parts and members in the same part are interrelated whilein different parts there are no interrelationships among members, and develop some new operationalrules of LIFNs to consider the interactions between membership function and non-membership function,especially when the degree of non-membership is zero. Then we extend PHM operator to LIFNs based onnew operational rules, and propose the linguistic intuitionistic fuzzy partitioned Heronian mean (LIFPHM)operator, the linguistic intuitionistic fuzzy weighted partitioned Heronian mean (LIFWPHM) operator,the linguistic intuitionistic fuzzy partitioned geometric Heronian mean (LIFPGHM) operator and linguis-tic intuitionistic fuzzy weighted partitioned geometric Heronian mean (LIFWPGHM) operator. Further,we develop two methods to solve multi-attribute group decision making (MAGDM) problems with thelinguistic intuitionistic fuzzy information. Finally, we give some examples to verify the effectiveness oftwo proposed methods by comparing with the existing
National Natural Science Foundation of China (Nos. 71771140 and 71471172), the Special Funds of TaishanScholars Project of Shandong Province (No. ts201511045), Shandong Provincial Social Science Planning Project (Nos. 15BGLJ06,16CGLJ31and 16CKJJ27), the Natural Science Foundation of Shandong Province (No. ZR2017MG007), the Teaching Reform ResearchProject of Undergraduate Colleges and Universities in Shandong Province (No. 2015Z057),and Key research anddevelopment program of Shandong Province(No. 2016GNC110016). The third author acknowledges the Distinguished Scientist Fellowship Program of the King Saud University (SaudiArabia).
Artículo de publicación ISI
Quote ItemApplied Soft Computing, 62 (2018): 395–422
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