A novel fuzzy stochastic MAGDM method based on credibility theory and fuzzy stochastic dominance with incomplete weight information

With respect to multiple attribute group decision-making (MAGDM) in which the assessment values of alternatives are denoted by normal discrete fuzzy variables (NDFVs) and the weight information of attributes is incompletely known, this paper aims to develop a novel fuzzy stochastic MAGDM method based on credibility theory and fuzzy stochastic dominance, and then applies the proposed method for selecting the most desirable investment alternative under uncertain environment.,First, by aggregating the membership degrees of an alternative to a scale provided by all decision-makers into a triangular fuzzy number, the credibility degree and expect the value of a triangular fuzzy number are calculated to construct the group fuzzy stochastic decision matrix. Second, based on determining the credibility distribution functions of NDFVs, the fuzzy stochastic dominance relations between alternatives on each attribute are obtained and the fuzzy stochastic dominance degree matrices are constructed by calculating the dominance degrees that one alternative dominates another on each attribute. Subsequently, calculating the overall fuzzy stochastic dominance degrees of an alternative on each attribute, a single objective non-linear optimization model is established to determine the weights of attributes by maximizing the relative closeness coefficients of all alternatives to positive ideal solution. If the information about attribute weights is completely unknown, the idea of maximizing deviation is used to determine the weights of attributes. Finally, the ranking order of alternatives is determined according to the descending order of corresponding relative closeness coefficients and the best alternative is determined.,This paper proposes a novel fuzzy stochastic MAGDM method based on credibility theory and fuzzy stochastic dominance, and a case study of investment alternative selection problem is provided to illustrate the applicability and sensitivity of the proposed method and its effectiveness is demonstrated by comparison analysis with the proposed method with the existing fuzzy stochastic MAGDM method. The result shows that the proposed method is useful to solve the MAGDM problems in which the assessment values of alternatives are denoted by NDFVs and the weight information of attributes is incompletely known.,The contributions of this paper are that to describe the dominance relations between fuzzy variables reasonably and quantitatively, the fuzzy stochastic dominance relations between any two fuzzy variables are redefined and the concept of fuzzy stochastic dominance degree is proposed to measure the dominance degree that one fuzzy variable dominate another; Based on credibility theory and fuzzy stochastic dominance, a novel fuzzy stochastic MAGDM method is proposed to solve MAGDM problems in which the assessment values of alternatives are denoted by NDFVs and the weight information of attributes is incompletely known. The proposed method has a clear logic, which not only can enrich and develop the theories and methods of MAGDM but also provides decision-makers a novel method for solving fuzzy stochastic MAGDM problems.

[1]  Didier Dubois,et al.  Ranking fuzzy numbers in the setting of possibility theory , 1983, Inf. Sci..

[2]  Yejun Xu,et al.  Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making , 2016, Knowl. Based Syst..

[3]  Jian-qiang Wang,et al.  An approach to interval-valued intuitionistic stochastic multi-criteria decision-making using set pair analysis , 2018, Int. J. Mach. Learn. Cybern..

[4]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[5]  Enrique Herrera-Viedma,et al.  Soft consensus measures in group decision making using unbalanced fuzzy linguistic information , 2017, Soft Comput..

[6]  K. S. Park,et al.  Mathematical programming models for characterizing dominance and potential optimality when multicriteria alternative values and weights are simultaneously incomplete , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  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..

[8]  Cunbin Li,et al.  A New Method for Multi-Attribute Decision Making with Intuitionistic Trapezoidal Fuzzy Random Variable , 2017, Int. J. Fuzzy Syst..

[9]  K. Zaras,et al.  Stochastic dominance in multicriterion analysis under risk , 1995 .

[10]  Guiwu Wei,et al.  Some prioritized aggregating operators with linguistic information and their application to multiple attribute group decision making , 2014, J. Intell. Fuzzy Syst..

[11]  Chung-Hsing Yeh,et al.  Modeling subjective evaluation for fuzzy group multicriteria decision making , 2009, Eur. J. Oper. Res..

[12]  Bingzhen Sun,et al.  An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application , 2015 .

[13]  Ferenc Szidarovszky,et al.  Revising the OWA operator for multi criteria decision making problems under uncertainty , 2009, Eur. J. Oper. Res..

[14]  E. Lee,et al.  Comparison of fuzzy numbers based on the probability measure of fuzzy events , 1988 .

[15]  Xiang Li,et al.  On distance between fuzzy variables , 2008, J. Intell. Fuzzy Syst..

[16]  Kyung S. Park,et al.  Tools for interactive multiattribute decisionmaking with incompletely identified information , 1997 .

[17]  Sanjay Kumar,et al.  Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making , 2016, Eur. J. Oper. Res..

[18]  D. Dubois,et al.  The mean value of a fuzzy number , 1987 .

[19]  Wei Zhang,et al.  European Journal of Operational Research an Interval-valued Intuitionistic Fuzzy Principal Component Analysis Model-based Method for Complex Multi-attribute Large-group Decision-making , 2022 .

[20]  Mao-Jiun J. Wang,et al.  Ranking fuzzy numbers with integral value , 1992 .

[21]  Stanisław Heilpern,et al.  The expected value of a fuzzy number , 1992 .

[22]  H. B. Mitchell,et al.  On ordering fuzzy numbers , 2000, International Journal of Intelligent Systems.

[23]  Mahdi Zarghami,et al.  A fuzzy-stochastic OWA model for robust multi-criteria decision making , 2008, Fuzzy Optim. Decis. Mak..

[24]  Ronald R. Yager,et al.  A procedure for ordering fuzzy subsets of the unit interval , 1981, Inf. Sci..

[25]  José M. Merigó,et al.  Subjective and objective information in linguistic multi-criteria group decision making , 2016, Eur. J. Oper. Res..

[26]  M. Izadikhah,et al.  A novel method to extend SAW for decision-making problems with interval data , 2014 .

[27]  Zeshui Xu,et al.  An interactive method for fuzzy multiple attribute group decision making , 2007, Inf. Sci..

[28]  Yian-Kui Liu,et al.  Expected value of fuzzy variable and fuzzy expected value models , 2002, IEEE Trans. Fuzzy Syst..

[29]  Reza Tavakkoli-Moghaddam,et al.  A Fuzzy Stochastic Multi-Attribute Group Decision-Making Approach for Selection Problems , 2011, Group Decision and Negotiation.

[30]  Soung Hie Kim,et al.  Interactive group decision making procedure under incomplete information , 1999, Eur. J. Oper. Res..