On admissible total orders for typical hesitant fuzzy consensus measures

In this paper, we discussconsensus measures for typical hesitant fuzzy elements (THFE), which are the finite and nonempty fuzzy membership degrees under the scope of typical hesitant fuzzy sets (THFS). In our approach, we present a model that formally constructs consensus measures by means of aggregations functions, fuzzy implication‐like functions and fuzzy negations, using admissible orders to compare the THFE, and also providing an analysis of consistency on them. Our theoretical results are applied into a problem of decision making with multicriteria illustrating our methodology to achieve consensus in a group of experts working with THFS.

[1]  Noor Rehman,et al.  A more efficient conflict analysis based on soft preference relation , 2018, J. Intell. Fuzzy Syst..

[2]  Zeshui Xu,et al.  A consensus process for group decision making with probabilistic linguistic preference relations , 2017, Inf. Sci..

[3]  Witold Pedrycz,et al.  A review of soft consensus models in a fuzzy environment , 2014, Inf. Fusion.

[4]  Zeshui Xu,et al.  Admissible orders of typical hesitant fuzzy elements and their application in ordered information fusion in multi-criteria decision making , 2016, Inf. Fusion.

[5]  Humberto Bustince,et al.  Typical Hesitant Fuzzy Negations , 2014, Int. J. Intell. Syst..

[6]  Zeshui Xu,et al.  Regression methods for hesitant fuzzy preference relations , 2014 .

[7]  Francisco Herrera,et al.  Some issues on consistency of fuzzy preference relations , 2004, Eur. J. Oper. Res..

[8]  Enrique Herrera-Viedma,et al.  A comparative study on consensus measures in group decision making , 2018, Int. J. Intell. Syst..

[9]  Humberto Bustince,et al.  On admissible orders over closed subintervals of [0, 1] , 2020, Fuzzy Sets Syst..

[10]  Hongying Zhang,et al.  Inclusion measure for typical hesitant fuzzy sets, the relative similarity measure and fuzzy entropy , 2016, Soft Comput..

[11]  James C. Bezdek,et al.  A Fuzzy Analysis of Consensus in Small Groups , 1980 .

[12]  Zeshui Xu,et al.  Hesitant Fuzzy Sets Theory , 2014, Studies in Fuzziness and Soft Computing.

[13]  Vicenç Torra,et al.  Hesitant fuzzy sets , 2010, Int. J. Intell. Syst..

[14]  Jian Chen,et al.  Consistency and consensus improving methods for pairwise comparison matrices based on Abelian linearly ordered group , 2015, Fuzzy Sets Syst..

[15]  Humberto Bustince,et al.  Strategies on admissible total orders over typical hesitant fuzzy implications applied to decision making problems , 2021, Int. J. Intell. Syst..

[16]  Vicenç Torra,et al.  On hesitant fuzzy sets and decision , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[17]  Zeshui Xu,et al.  Managing hesitant Information in GDM Problems under fuzzy and Multiplicative Preference Relations , 2013, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[18]  José Luis García-Lapresta,et al.  Measuring consensus in a preference-approval context , 2014, Inf. Fusion.

[19]  Deyu Li,et al.  Interval-valued hesitant fuzzy multi-granularity three-way decisions in consensus processes with applications to multi-attribute group decision making , 2020, Inf. Sci..

[20]  R. Bosch Characterizations on voting rules and consensus measures , 2006 .

[21]  Humberto Bustince,et al.  Typical hesitant fuzzy negations based on Xu-Xia-partial order , 2014, 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW).

[22]  Gleb Beliakov,et al.  Consensus measures constructed from aggregation functions and fuzzy implications , 2014, Knowl. Based Syst..

[23]  Yejun Xu,et al.  Exploring Consistency for Hesitant Preference Relations in Decision Making: Discussing Concepts, Meaning and Taxonomy , 2018, J. Multiple Valued Log. Soft Comput..

[24]  Noor Rehman,et al.  Soft dominance based rough sets with applications in information systems , 2019, Int. J. Approx. Reason..

[25]  Noor Rehman,et al.  SDMGRS: Soft Dominance Based Multi Granulation Rough Sets and Their Applications in Conflict Analysis Problems , 2018, IEEE Access.

[26]  Renata Reiser,et al.  Typical Hesitant Fuzzy Sets: Evaluating Strategies in GDM Applying Consensus Measures , 2019, EUSFLAT Conf..

[27]  Hiok Chai Quek,et al.  Curvature-based method for determining the number of clusters , 2017, Inf. Sci..

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

[29]  Humberto Bustince,et al.  Aggregation functions for typical hesitant fuzzy elements and the action of automorphisms , 2014, Inf. Sci..