On extension of multiplicative consistency to interval fuzzy preference relations

Various definitions of consistency for interval fuzzy preference relations have been proposed in the literature. The aim of this paper is to review the definitions of multiplicative consistency based on the extension of Tanino’s multiplicative-transitivity property and to point out their drawbacks. In particular, some of the definitions proposed in the literature are not invariant under permutation of objects in interval fuzzy preference relations and some of them violate reciprocity of the pairwise comparisons of objects, which is not acceptable. The weak form of multiplicative-consistency defined by Xu and Chen (Eur J Oper Res 184:266–280, 2008) is approved as the only one appropriate among all definitions examined in the paper. Further, a new definition of multiplicative consistency that is much stronger than the definition proposed by Xu and Chen (Eur J Oper Res 184:266–280, 2008) is introduced. Tools for verifying both the multiplicative consistency defined in this paper and the multiplicative weak consistency defined by Xu and Chen (Eur J Oper Res 184:266–280, 2008) are proposed, and some interesting properties of both multiplicatively consistent and multiplicatively weakly consistent interval fuzzy preference relations are demonstrated. Finally, numerical examples are provided in order to illustrate and compare both types of consistency.

[1]  Jana Talasová,et al.  A fuzzy extension of Analytic Hierarchy Process based on the constrained fuzzy arithmetic , 2016, Fuzzy Optimization and Decision Making.

[2]  Zeshui Xu,et al.  Interval multiplicative transitivity for consistency, missing values and priority weights of interval fuzzy preference relations , 2010, Inf. Sci..

[3]  Enrique Herrera-Viedma,et al.  Confidence-consistency driven group decision making approach with incomplete reciprocal intuitionistic preference relations , 2015, Knowl. Based Syst..

[4]  Enrique Herrera-Viedma,et al.  Managing incomplete preference relations in decision making: A review and future trends , 2015, Inf. Sci..

[5]  Mario Enea,et al.  Project Selection by Constrained Fuzzy AHP , 2004, Fuzzy Optim. Decis. Mak..

[6]  Jana Krejčí,et al.  Obtaining fuzzy priorities from additive fuzzy pairwise comparison matrices , 2016 .

[7]  Enrique Herrera-Viedma,et al.  Average-case consistency measurement and analysis of interval-valued reciprocal preference relations , 2016, Knowl. Based Syst..

[8]  Zhou-Jing Wang,et al.  Goal programming approaches to deriving interval weights based on interval fuzzy preference relations , 2012, Inf. Sci..

[9]  Zhou-Jing Wang,et al.  Consistency analysis and priority derivation of triangular fuzzy preference relations based on modal value and geometric mean , 2015, Inf. Sci..

[10]  Francisco Chiclana,et al.  A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations , 2014, Knowl. Based Syst..

[11]  Francisco Herrera,et al.  Group Decision-Making Model With Incomplete Fuzzy Preference Relations Based on Additive Consistency , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Francisco Chiclana,et al.  Multiplicative consistency of intuitionistic reciprocal preference relations and its application to missing values estimation and consensus building , 2014, Knowl. Based Syst..

[13]  Zhou-Jing Wang,et al.  A note on "Incomplete interval fuzzy preference relations and their applications" , 2014, Comput. Ind. Eng..

[14]  S. Orlovsky Decision-making with a fuzzy preference relation , 1978 .

[15]  George J. Klir,et al.  Constrained fuzzy arithmetic: Basic questions and some answers , 1998, Soft Comput..

[16]  Jana Krej On additive consistency of interval fuzzy preference relations , 2017 .

[17]  Yejun Xu,et al.  Incomplete interval fuzzy preference relations and their applications , 2014, Comput. Ind. Eng..

[18]  Jun Wang,et al.  Some programming models to derive priority weights from additive interval fuzzy preference relation , 2012, Knowl. Based Syst..

[19]  Jana Krejcí,et al.  Additively reciprocal fuzzy pairwise comparison matrices and multiplicative fuzzy priorities , 2017, Soft Comput..

[20]  Fang Liu,et al.  Acceptable consistency analysis of interval reciprocal comparison matrices , 2009, Fuzzy Sets Syst..

[21]  Michele Fedrizzi,et al.  Axiomatic properties of inconsistency indices for pairwise comparisons , 2013, J. Oper. Res. Soc..

[22]  Zeshui Xu,et al.  Some Issues on Multiplicative Consistency of Interval Reciprocal Relations , 2011, Int. J. Inf. Technol. Decis. Mak..

[23]  Zeshui Xu,et al.  Note on “Some models for deriving the priority weights from interval fuzzy preference relations” , 2008 .

[24]  F. Herrera,et al.  A consistency-based procedure to estimate missing pairwise preference values , 2008 .

[25]  Zeshui Xu,et al.  Multiple-Attribute Group Decision Making With Different Formats of Preference Information on Attributes , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Fang Liu,et al.  A new method of obtaining the priority weights from an interval fuzzy preference relation , 2012, Inf. Sci..

[27]  Jana Krej,et al.  Fuzzy eigenvector method for obtaining normalized fuzzy weights from fuzzy pairwise comparison matrices , 2017 .

[28]  T. Tanino Fuzzy preference orderings in group decision making , 1984 .

[29]  Stephen Lin,et al.  Rank-One Projections With Adaptive Margins for Face Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  J. Kacprzyk Group decision making with a fuzzy linguistic majority , 1986 .

[31]  H. Nurmi Approaches to collective decision making with fuzzy preference relations , 1981 .

[32]  Francisco Herrera,et al.  Cardinal Consistency of Reciprocal Preference Relations: A Characterization of Multiplicative Transitivity , 2009, IEEE Transactions on Fuzzy Systems.

[33]  Yejun Xu,et al.  Consistency test and weight generation for additive interval fuzzy preference relations , 2013, Soft Computing.

[34]  Weldon A. Lodwick,et al.  Constrained intervals and interval spaces , 2013, Soft Comput..

[35]  Michele Fedrizzi,et al.  A Note on the Paper "Fuzzy Analytic Hierarchy Process: Fallacy of the Popular Methods" , 2015, Int. J. Uncertain. Fuzziness Knowl. Based Syst..