Sensitivity Analysis to Attribute Values Based on Grey Correlation in Multiple Attribute Decision Making

2013 ◽  
Vol 850-851 ◽  
pp. 927-931
Author(s):  
Zheng Dong Huang ◽  
Hong Xiang Feng
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Search Algorithm ◽  
Multiple Attribute Decision Making ◽  
Analysis Model ◽  
Grey Correlation ◽  
Numerical Example ◽  
Multiple Attribute ◽  
Variation Interval

With respect to the multiple attribute decision making problems based on grey correlation, sensitivity analysis is proceeded to attribute values, and analysis model as well as search algorithm are given to compute the attribute variation interval which maintain the original rank. Finally, a numerical example was presented.

Sensitivity Analysis to Attribute Values Based on Grey Correlation in Multiple Attribute Decision Making and its Application

2013 ◽  
Vol 709 ◽  
pp. 620-623
Author(s):  
Hong Xiang Feng ◽  
Ying Jie Xiao ◽  
Fan Cun Kong
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Search Algorithm ◽  
Multiple Attribute Decision Making ◽  
Analysis Model ◽  
Grey Correlation ◽  
Multiple Attribute

With respect to the multiple attribute decision making problems based on grey correlation, sensitivity analysis is proceeded to attribute values, and analysis model as well as search algorithm are proposed to compute the attribute variation intervals which preserve the original ranking. Finally, an example is presented to demonstrate its application.

Possibility-Based ELECTRE II Method with Uncertain Linguistic Fuzzy Variables

2017 ◽  
Vol 31 (09) ◽  
pp. 1759016 ◽  
Author(s):  
Lidong Wang ◽  
Binquan Liao ◽  
Xiaodong Liu ◽  
Jingxia Liu
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Economic Systems ◽  
Linguistic Variables ◽  
Concordance Index ◽  
Fuzzy Variables ◽  
Numerical Example ◽  
Deviation Index ◽  
Multiple Attribute

Linguistic variables can better approximate the fuzziness of man’s thinking, which are important tools for multiple attribute decision-making problems. This paper establishes the possibility-based ELECTRE II model under the environment of uncertain linguistic fuzzy variables and uncertain weight information. By introducing the degree of possibility to ELECTRE II model, the concordance set, the discordance set and the indifferent set are obtained, respectively. Furthermore, the concordance index is redefined by considering deviation index under the same attribute, by which the strong and weak relationships are constructed, and then the rank of alternatives is obtained. A numerical example about the evaluation of socio-economic systems is employed to illustrate the convenience and applicability of the proposed method.

Multiple Attribute Decision-Making Methods with Unbalanced Linguistic Variables Based on Maclaurin Symmetric Mean Operators

2019 ◽  
Vol 18 (01) ◽  
pp. 105-146 ◽  
Author(s):  
Fei Teng ◽  
Peide Liu ◽  
Li Zhang ◽  
Juan Zhao
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Linguistic Variables ◽  
Numerical Example ◽  
Maclaurin Symmetric Mean ◽  
Multiple Attribute ◽  
Linguistic Term

In this paper, we firstly introduced the unbalanced linguistic term sets, the linguistic transforming methodology, the Maclaurin symmetric mean (MSM) operator and dual MSM (DMSM) operator. Then, we proposed the closed operational rules of unbalanced linguistic variables, and several new MSM aggregation operators, including unbalanced linguistic MSM (ULMSM) operator, weighted unbalanced linguistic MSM (WULMSM) operator, unbalanced linguistic DMSM (ULDMSM) operator and weighted unbalanced linguistic DMSM (WULDMSM) operator. Further, we proposed two multiple attribute decision-making (MADM) methods under unbalanced linguistic environments based on the WULMSM operator and WULDMSM operator, respectively. Finally, a numerical example is used to show the applicability and effectiveness of the proposed MADM methods and to reveal their advantages by comparing with the existing methods.

scholarly journals Hesitant Fuzzy Generalised Bonferroni Mean Operators Based on Archimedean Copula for Multiple-Attribute Decision-Making

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ju Wu ◽  
Fang Liu ◽  
Yuan Rong ◽  
Yi Liu ◽  
Chengxi Liu
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Information Fusion ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operator ◽  
Archimedean Copula ◽  
Fuzzy Information ◽  
Bonferroni Mean ◽  
Operation Rules ◽  
Multiple Attribute

Information fusion is an important part of multiple-attribute decision-making, and aggregation operator is an important tool of decision information fusion. Integration operators in a variety of fuzzy information environments have a slight lack of consideration for the correlation between variables. Archimedean copula provides information fusion patterns that rely on the intrinsic relevance of information. This paper extends the Archimedean copula to the aggregation of hesitant fuzzy information. Firstly, the Archimedean copula is used to generate the operation rules of the hesitant fuzzy elements. Secondly, the hesitant fuzzy copula Bonferroni mean operator and hesitant fuzzy weighted copula Bonferroni mean operator are propounded, and several properties are proved in detail. Furthermore, a decision-making method based on the operators is proposed, and the specific decision steps are given. Finally, an example is presented to illustrate the practical advantages of the method, and the sensitivity analysis of the decision results with the change of parameters is carried out.

scholarly journals Dominance Degree Multiple Attribute Decision Making Based on Z-Number Cognitive Information

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Aiting Yao ◽  
Junjun Mao ◽  
Minchao Wu ◽  
Tao Wu
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Decision Model ◽  
Multiple Attribute Decision Making ◽  
Cross Entropy ◽  
Probability Models ◽  
Multiple Attribute ◽  
Cognitive Information ◽  
Relationship Of ◽  
The Impact

Cognitive information can be described by Z-number fully and effectively. However, many problems of Z-number need to be further studied. In this paper, two hidden probability models for calculating Z-number are established to provide more intuitive and abundant information. Next, the dominance degree relationship of Z-number is developed and subdivided. Furthermore, combined with the hidden probability of calculation, three different measurements of dominance degree are defined from three levels of geometry, algebra, and cross entropy based on the outranking relationship. The influencing factors are analyzed for different combinations of two probability models and three dominance degree measures. A multiattribute decision model is established on the basis of new grey association analysis and QUALIFLEX method. Finally, a decision example is given to verify the effectiveness and feasibility of the method. And sensitivity analysis is made to determine the impact of parameters and hidden probability on the decision model.

scholarly journals Bipolar Complex Fuzzy Hamacher Aggregation Operators and Their Applications in Multi-Attribute Decision Making

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Tahir Mahmood ◽  
Ubaid ur Rehman ◽  
Jabbar Ahmmad ◽  
Gustavo Santos-García
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Numerical Example ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Ordered Weighted Average

On the basis of Hamacher operations, in this manuscript, we interpret bipolar complex fuzzy Hamacher weighted average (BCFHWA) operator, bipolar complex fuzzy Hamacher ordered weighted average (BCFHOWA) operator, bipolar complex fuzzy Hamacher hybrid average (BCFHHA) operator, bipolar complex fuzzy Hamacher weighted geometric (BCFHWG) operator, bipolar complex fuzzy Hamacher ordered weighted geometric (BCFHOWG) operator, and bipolar complex fuzzy Hamacher hybrid geometric (BCFHHG) operator. We present the features and particular cases of the above-mentioned operators. Subsequently, we use these operators for methods that can resolve bipolar complex fuzzy multiple attribute decision making (MADM) issues. We provide a numerical example to authenticate the interpreted methods. In the end, we compare our approach with existing methods in order to show its effectiveness and practicality.

scholarly journals A Distance Model of Intuitionistic Fuzzy Cross Entropy to Solve Preference Problem on Alternatives

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mei Li ◽  
Chong Wu
Keyword(s):  
Decision Making ◽  
Correlation Analysis ◽  
Distance Measure ◽  
Multiple Attribute Decision Making ◽  
Cross Entropy ◽  
Grey Correlation ◽  
Attribute Weights ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Grey Correlation Analysis

In the field of decision-making, for the multiple attribute decision-making problem with the partially unknown attribute weights, the evaluation information in the form of the intuitionistic fuzzy numbers, and the preference on alternatives, this paper proposes a comprehensive decision model based on the intuitionistic fuzzy cross entropy distance and the grey correlation analysis. The creative model can make up the deficiency that the traditional intuitionistic fuzzy distance measure is easy to cause the confusion of information and can improve the accuracy of distance measure; meanwhile, the grey correlation analysis method, suitable for the small sample and the poor information decision-making, is applied in the evaluation. This paper constructs a mathematical optimization model of maximizing the synthesis grey correlation coefficient between decision-making evaluation values and decision-makers’ subjective preference values, calculates the attribute weights with the known partial weight information, and then sorts the alternatives by the grey correlation coefficient values. Taking venture capital firm as an example, through the calculation and the variable disturbance, we can see that the methodology used in this paper has good stability and rationality. This research makes the decision-making process more scientific and further improves the theory of intuitionistic fuzzy multiple attribute decision-making.

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