Some q‐Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple‐Attribute Decision Making

The q‐rung orthopair fuzzy sets (q‐ROFs) are an important way to express uncertain information, and they are superior to the intuitionistic fuzzy sets and the Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the membership degree and the qth power of the degrees of non‐membership is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we propose the q‐rung orthopair fuzzy weighted averaging operator and the q‐rung orthopair fuzzy weighted geometric operator to deal with the decision information, and their some properties are well proved. Further, based on these operators, we presented two new methods to deal with the multi‐attribute decision making problems under the fuzzy environment. Finally, we used some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

[1]  Zeshui Xu Intuitionistic Fuzzy Multiattribute Decision Making: An Interactive Method , 2012, IEEE Transactions on Fuzzy Systems.

[2]  Zeshui Xu,et al.  Power-Geometric Operators and Their Use in Group Decision Making , 2010, IEEE Transactions on Fuzzy Systems.

[3]  Ronald R. Yager,et al.  Pythagorean Membership Grades in Multicriteria Decision Making , 2014, IEEE Transactions on Fuzzy Systems.

[4]  Peide Liu,et al.  Some Generalized Einstein Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making , 2015 .

[5]  Ranjit Biswas,et al.  Some operations on intuitionistic fuzzy sets , 2000, Fuzzy Sets Syst..

[6]  Peide Liu,et al.  Some Hamacher Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making , 2014, IEEE Transactions on Fuzzy Systems.

[7]  Zeshui Xu,et al.  Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets , 2014, Int. J. Intell. Syst..

[8]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[9]  Shuai Zhang,et al.  Combined Approach for Government E-Tendering Using GA and TOPSIS with Intuitionistic Fuzzy Information , 2015, PloS one.

[10]  Ronald R. Yager,et al.  Pythagorean Membership Grades, Complex Numbers, and Decision Making , 2013, Int. J. Intell. Syst..

[11]  Peide Liu,et al.  Research on the supplier selection of a supply chain based on entropy weight and improved ELECTRE-III method , 2011 .

[12]  Xin Zhang,et al.  Multiple attribute group decision making methods based on intuitionistic fuzzy frank power aggregation operators , 2015, J. Intell. Fuzzy Syst..

[13]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[14]  Zeshui Xu,et al.  Intuitionistic Fuzzy Bonferroni Means , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Qi Song,et al.  On the entropy for Atanassov's intuitionistic fuzzy sets: An interpretation from the perspective of amount of knowledge , 2014, Appl. Soft Comput..

[16]  Yong Yang,et al.  Some Results for Pythagorean Fuzzy Sets , 2015, Int. J. Intell. Syst..

[17]  Harish Garg,et al.  Generalized Pythagorean Fuzzy Geometric Aggregation Operators Using Einstein t‐Norm and t‐Conorm for Multicriteria Decision‐Making Process , 2017, Int. J. Intell. Syst..

[18]  Huayou Chen,et al.  Intuitionistic fuzzy geometric interaction averaging operators and their application to multi-criteria decision making , 2014, Inf. Sci..

[19]  Jen-Ping Peng,et al.  The incorporation of the Taguchi and the VIKOR methods to optimize multi-response problems in intuitionistic fuzzy environments , 2015 .

[20]  Zeshui Xu,et al.  Pythagorean fuzzy TODIM approach to multi-criteria decision making , 2016, Appl. Soft Comput..

[21]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[22]  Zeshui Xu,et al.  Some geometric aggregation operators based on intuitionistic fuzzy sets , 2006, Int. J. Gen. Syst..

[23]  Jianping Yuan,et al.  Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China , 2016 .

[24]  N. Malys,et al.  Comparative analysis of MCDM methods for the assessment of sustainable housing affordability , 2016 .

[25]  Li,et al.  AN INTUITIONISTIC FUZZY-TODIM METHOD TO SOLVE DISTRIBUTOR EVALUATION AND SELECTION PROBLEM , 2015 .

[26]  Shyi-Ming Chen,et al.  A novel similarity measure between Atanassov's intuitionistic fuzzy sets based on transformation techniques with applications to pattern recognition , 2015, Inf. Sci..

[27]  Hoang Nguyen,et al.  A new knowledge-based measure for intuitionistic fuzzy sets and its application in multiple attribute group decision making , 2015, Expert Syst. Appl..

[28]  Harish Garg,et al.  A New Generalized Pythagorean Fuzzy Information Aggregation Using Einstein Operations and Its Application to Decision Making , 2016, Int. J. Intell. Syst..

[29]  Ronald R. Yager,et al.  Generalized Orthopair Fuzzy Sets , 2017, IEEE Transactions on Fuzzy Systems.

[30]  Ting-Yu Chen,et al.  A note on distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric , 2007, Fuzzy Sets Syst..

[31]  K. Atanassov More on intuitionistic fuzzy sets , 1989 .

[32]  Yildiz Esra Albayrak,et al.  An engineering approach to human resources performance evaluation: Hybrid MCDM application with interactions , 2014, Appl. Soft Comput..

[33]  Janusz Kacprzyk,et al.  Distances between intuitionistic fuzzy sets , 2000, Fuzzy Sets Syst..