On the use of Hesitant Fuzzy Linguistic Term Set in FLINTSTONES

The use of linguistic information to model and manage uncertainty in Decision Making (DM) has been a key subject of many proposals in the literature. The 2-tuple linguistic model and its extensions in linguistic DM has been very successful and extensive due to their flexibility and accuracy. Flintstones is a novel fuzzy linguistic decision tool enhancement suite that implements tools to facilitate the solving of linguistic DM problems that model the linguistic information with such a model and its extensions. However, both the 2-tuple linguistic model and Flintstones can not deal with uncertain situations modelled linguistically in which experts hesitate among several linguistic terms. For these cases, recently, it has been proposed the use of Hesitant Fuzzy Linguistic Term Sets (HFLTS) that have attracted a lot of research interest, mainly regarding its application in DM. Hence in this contribution it is proposed an extended version of Flintstones that includes the ability and functionality of dealing with HFLTS in linguistic decision problems and enables the integration, validity and performance of hesitant linguistic decision models and operators.

[1]  Tabasam Rashid,et al.  TOPSIS for Hesitant Fuzzy Linguistic Term Sets , 2013, Int. J. Intell. Syst..

[2]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[3]  Francisco Herrera,et al.  A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Francisco Herrera,et al.  Managing non-homogeneous information in group decision making , 2005, Eur. J. Oper. Res..

[5]  Zeshui Xu,et al.  An overview of methods for determining OWA weights , 2005, Int. J. Intell. Syst..

[6]  Francisco Herrera,et al.  An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges , 2012, Inf. Sci..

[7]  Hongbin Liu,et al.  A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making , 2014, Inf. Sci..

[8]  Macarena Espinilla,et al.  Fuzzy Linguistic Olive Oil Sensory Evaluation Model based on Unbalanced Linguistic Scales , 2014, J. Multiple Valued Log. Soft Comput..

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

[10]  Yildiz Esra Albayrak,et al.  Criteria Weighting and 4P's Planning in Marketing Using a Fuzzy Metric Distance and AHP Hybrid Method , 2014, Int. J. Comput. Intell. Syst..

[11]  V. E. Zhukovin,et al.  A Fuzzy Multicriteria Decision Making Model , 1987 .

[12]  Francisco Herrera,et al.  Computing with Words in Decision support Systems: An overview on Models and Applications , 2010, Int. J. Comput. Intell. Syst..

[13]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[14]  Jerry M. Mendel,et al.  What Computing with Words Means to Me [Discussion Forum] , 2010, IEEE Computational Intelligence Magazine.

[15]  Na Zhao,et al.  Operators and Comparisons of Hesitant Fuzzy Linguistic Term Sets , 2014, IEEE Transactions on Fuzzy Systems.

[16]  Alp Üstündag A Fuzzy Risk Assessment Model for Warehouse Operations , 2014, J. Multiple Valued Log. Soft Comput..

[17]  Francisco Herrera,et al.  A fusion approach for managing multi-granularity linguistic term sets in decision making , 2000, Fuzzy Sets Syst..

[18]  Zeshui Xu,et al.  Consistency Measures for Hesitant Fuzzy Linguistic Preference Relations , 2014, IEEE Transactions on Fuzzy Systems.

[19]  Robert T. Clemen,et al.  Making Hard Decisions: An Introduction to Decision Analysis , 1997 .

[20]  Luis Martínez-López,et al.  AN EXTENDED HIERARCHICAL LINGUISTIC MODEL FOR DECISION‐MAKING PROBLEMS , 2011, Comput. Intell..

[21]  Francisco Herrera,et al.  A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets , 2013, Inf. Sci..

[22]  Van-Nam Huynh,et al.  A satisfactory-oriented approach to multiexpert decision-making with linguistic assessments , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Javier Montero,et al.  Computational intelligence in decision making , 2014, Int. J. Comput. Intell. Syst..

[24]  Luis Martínez-López,et al.  An analysis of symbolic linguistic computing models in decision making , 2013, Int. J. Gen. Syst..

[25]  Deng-Feng Li,et al.  A systematic approach to heterogeneous multiattribute group decision making , 2010, Comput. Ind. Eng..

[26]  Francisco Herrera,et al.  The 2-Tuple Linguistic Computational Model. Advantages of Its Linguistic Description, Accuracy and Consistency , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[27]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[28]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[29]  Evangelos Triantaphyllou,et al.  Multi-criteria Decision Making Methods: A Comparative Study , 2000 .

[30]  Alessio Ishizaka,et al.  Multi-criteria Decision Analysis: Methods and Software , 2013 .

[31]  R. Clemen,et al.  Soft Computing , 2002 .

[32]  Witold Pedrycz,et al.  Fuzzy Multicriteria Decision-Making: Models, Methods and Applications , 2010 .

[33]  Enrique Herrera-Viedma,et al.  Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information , 2010, Knowl. Based Syst..

[34]  Francisco Herrera,et al.  A Fuzzy Linguistic Methodology to Deal With Unbalanced Linguistic Term Sets , 2008, IEEE Transactions on Fuzzy Systems.