Interval-valued probabilistic hesitant fuzzy set for multi-criteria group decision-making

As a powerful extension to fuzzy set, hesitant fuzzy set (HFS) attracted many scholars in the recent times. The HFS had the ability to accept multiple membership values for a specific instance, which helped in handling uncertainty to a certain extent. However, the previous studies on the hesitant fuzzy theory consider only single occurring probability value for each element which is problematic for decision-makers (DMs) to associate an accurate occurring probability with each element. To alleviate this issue, in this paper, a new concept called interval-valued probabilistic hesitant fuzzy set (IVPHFS) is proposed. Some desirable properties of IVPHFS are also investigated. Further, a new aggregation operator called simple interval-valued probabilistic hesitant fuzzy weighted geometry (SIVPHFWG) is presented and some interesting properties are discussed. Following this, a new extension of statistical variance (SV) is put forward under IVPHFS for calculating the weights of each criterion. A new extension to the popular VIKOR (VlseKriterijumskaOptimizacijaKompromisnoResenje) method is also presented under IVPHFS for ranking objects. The practicality of the proposed decision framework is analyzed by presenting two illustrative examples, viz., supplier selection problem and smartphone selection problem. Finally, the strength and weakness of the proposed decision framework are realized by comparison with other methods.

[1]  Yingyu Wu,et al.  An Improved Interval-Valued Hesitant Fuzzy Multi-Criteria Group Decision-Making Method and Applications , 2016 .

[2]  Qingguo Li,et al.  Multiple Attribute Decision Making Based on Hesitant Fuzzy Einstein Geometric Aggregation Operators , 2014, J. Appl. Math..

[3]  Zeshui Xu,et al.  An approach to hesitant fuzzy multi-stage multi-criterion decision making , 2014, Kybernetes.

[4]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .

[5]  Ronald R. Yager,et al.  Belief structures, weight generating functions and decision-making , 2017, Fuzzy Optim. Decis. Mak..

[6]  Zeshui Xu,et al.  Priorities of Intuitionistic Fuzzy Preference Relation Based on Multiplicative Consistency , 2014, IEEE Transactions on Fuzzy Systems.

[7]  Decui Liang,et al.  A Novel Risk Decision Making Based on Decision-Theoretic Rough Sets Under Hesitant Fuzzy Information , 2015, IEEE Transactions on Fuzzy Systems.

[8]  Qinggong Ma,et al.  Multi-attribute group decision making under probabilistic hesitant fuzzy environment with application to evaluate the transformation efficiency , 2018, Applied Intelligence.

[9]  Zhen He,et al.  Extensions of Atanassov's Intuitionistic Fuzzy Interaction Bonferroni Means and Their Application to Multiple-Attribute Decision Making , 2016, IEEE Transactions on Fuzzy Systems.

[10]  Francisco Rodrigues Lima Junior,et al.  A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection , 2014, Appl. Soft Comput..

[11]  Zeshui Xu,et al.  Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information , 2013, Knowl. Based Syst..

[12]  Huayou Chen,et al.  Hesitant Fuzzy Power Bonferroni Means and Their Application to Multiple Attribute Decision Making , 2015, IEEE Transactions on Fuzzy Systems.

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

[14]  Zeshui Xu,et al.  Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment , 2012, Inf. Fusion.

[15]  Zeshui Xu,et al.  Dual Hesitant Fuzzy Sets , 2012, J. Appl. Math..

[16]  Yingjun Zhang,et al.  Objective Attributes Weights Determining Based on Shannon Information Entropy in Hesitant Fuzzy Multiple Attribute Decision Making , 2014 .

[17]  C. Spearman The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.

[18]  Ahmad Makui,et al.  An extension on PROMETHEE based on the typical hesitant fuzzy sets to solve multi-attribute decision-making problem , 2016, Kybernetes.

[19]  Glin Bykzkan,et al.  Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem , 2017 .

[20]  T. Saaty,et al.  Why the magic number seven plus or minus two , 2003 .

[21]  Zeshui Xu,et al.  Operations and integrations of probabilistic hesitant fuzzy information in decision making , 2017, Inf. Fusion.

[22]  Zeshui Xu,et al.  Consensus building with a group of decision makers under the hesitant probabilistic fuzzy environment , 2016, Fuzzy Optimization and Decision Making.

[23]  Zeshui Xu,et al.  Hesitant fuzzy information aggregation in decision making , 2011, Int. J. Approx. Reason..

[24]  Mohammad Jafar Tarokh,et al.  A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting , 2011, Expert Syst. Appl..

[25]  Zeshui Xu,et al.  Subtraction and division operations over hesitant fuzzy sets , 2014, J. Intell. Fuzzy Syst..

[26]  Sen Liu,et al.  Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes , 2016, Expert Syst. Appl..

[27]  Qingguo Li,et al.  Hesitant Triangular Fuzzy Information Aggregation Operators Based on Bonferroni Means and Their Application to Multiple Attribute Decision Making , 2014, TheScientificWorldJournal.

[28]  Gwo-Hshiung Tzeng,et al.  Extended VIKOR method in comparison with outranking methods , 2007, Eur. J. Oper. Res..

[29]  Faramarz Hendessi,et al.  Using AHP and Interval VIKOR Methods to Gateway Selection in Integrated VANET and 3G Heterogeneous Wireless Networks in Sparse Situations , 2016 .

[30]  T. Saaty How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[31]  Tabasam Rashid,et al.  Group Decision Making Using Intuitionistic Hesitant Fuzzy Sets , 2014, Int. J. Fuzzy Log. Intell. Syst..

[32]  Wei Zhou,et al.  Probability Calculation and Element Optimization of Probabilistic Hesitant Fuzzy Preference Relations Based on Expected Consistency , 2018, IEEE Transactions on Fuzzy Systems.

[33]  Zeshui Xu,et al.  A VIKOR-based method for hesitant fuzzy multi-criteria decision making , 2013, Fuzzy Optimization and Decision Making.

[34]  Zeshui Xu,et al.  Consistency of the fused intuitionistic fuzzy preference relation in group intuitionistic fuzzy analytic hierarchy process , 2015, Appl. Soft Comput..

[35]  Gülçin Büyüközkan,et al.  Multi Criteria Group Decision Making Approach for Smart Phone Selection Using Intuitionistic Fuzzy TOPSIS , 2016, Int. J. Comput. Intell. Syst..

[36]  Gwo-Hshiung Tzeng,et al.  Exploring smart phone improvements based on a hybrid MCDM model , 2014, Expert Syst. Appl..

[37]  Hai Wang,et al.  Generalized hesitant fuzzy sets and their application in decision support system , 2013, Knowl. Based Syst..

[38]  Pankaj Gupta,et al.  Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based on a new extended VIKOR method , 2016, Inf. Sci..

[39]  Zeshui Xu,et al.  Hesitant fuzzy ELECTRE II approach: A new way to handle multi-criteria decision making problems , 2015, Inf. Sci..

[40]  Guiwu Wei,et al.  Hesitant fuzzy prioritized operators and their application to multiple attribute decision making , 2012, Knowl. Based Syst..

[41]  Zeshui Xu,et al.  Group consistency and group decision making under uncertain probabilistic hesitant fuzzy preference environment , 2017, Inf. Sci..

[42]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[43]  K. S. Ravichandran,et al.  A scientific decision-making framework for supplier outsourcing using hesitant fuzzy information , 2018, Soft Computing.

[44]  Zeshui Xu,et al.  Hesitant fuzzy QUALIFLEX approach with a signed distance-based comparison method for multiple criteria decision analysis , 2015, Expert Syst. Appl..

[45]  Zeshui Xu,et al.  Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment , 2017, Appl. Soft Comput..