scholarly journals Probabilistic Linguistic-Based Group DEMATEL Method with Both Positive and Negative Influences

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yonghe Sun ◽  
Siyu Zhang ◽  
Zihang Huang ◽  
Bin Miao
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Complex Systems ◽  
Negative Influence ◽  
Analysis Algorithm ◽  
Similarity Degree ◽  
Adjustment Method ◽  
Subjective Weight ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets

Decision-making trial and evaluation laboratory (DEMATEL) is a widely accepted factor analysis algorithm for complex systems. The rationality of the evaluation scale is the basis of sound DEMATEL decision-making. Unfortunately, the existing evaluation scales of DEMATEL failed to reasonably distinguish and describe the positive and negative influences between factors. Generally, the positive and negative influences between factors should be considered at the same time. In other words, negative influence between factors should not be directly ignored, which is improper and unrealistic. To better address this issue, we extend the evaluation scale of DEMATEL. We also integrate the scale-based group DEMATEL method with probabilistic linguistic term sets (PLTSs) to increase its effectiveness, which allows experts to express incomplete and uncertain linguistic preferences in DEMATEL decision-making. An experts’ subjective weight adjustment method based on the similarity degree between PLTSs is introduced to determine experts’ weights. Finally, an algorithm of probabilistic linguistic-based group DEMATEL method with both positive and negative influences is summarized, and an example is used to illustrate the proposed method and demonstrate its superiority. Our results demonstrate that the method proposed in this paper deals reasonably with realistic problems.

Transforming and decision-making based on probabilistic linguistic term sets with comparative linguistic expressions and incomplete assessments

2021 ◽  
Vol 40 (1) ◽  
pp. 491-506
Author(s):  
Ao Shen ◽  
Shuling Peng ◽  
Gaofei Liu
Keyword(s):  
Decision Making ◽  
Probability Distribution ◽  
Common Sense ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets ◽  
Probability Information ◽  
Incomplete Assessments

The probabilistic linguistic term sets (PLTSs) are widely used in decision-making, due to its convenience of evaluation, and allowances of probability information. However, there are still some cases where it is not convenient to give an evaluation using the PLTS gramma. Sometimes the evaluators can only give a comparative relationship between alternatives, sometimes evaluators may have difficulty understanding all the alternatives and cannot give a complete assessment. Therefore, we propose a method to transform the comparative linguistic expressions (CLEs) into PLTSs, and the comparison objects of CLEs are alternatives evaluated by PLTSs. And the probability distribution has been adjusted to make the transformation more in line with common sense. Then, a method to correct the deviation is proposed, allowing alternatives to be compared in the case of incomplete assessment. Combining the above two methods, we propose a decision-making method when both CLEs and incomplete assessments coexist. With the study in this paper, the limitations of PLTS-based evaluation and decision-making are reduced and the flexibility of using PLTS is improved.

scholarly journals Multiple Criteria Group Decision-Making Method with Dempster–Shafer Theory and Probabilistic Linguistic Term Sets

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yuanwei Du ◽  
Susu Wang
Keyword(s):  
Decision Making ◽  
Group Decision ◽  
Individual Level ◽  
Preference Theory ◽  
Dempster Shafer Theory ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets ◽  
The Individual ◽  
Shafer Theory ◽  
Interval Valued

The motivation of this study is to propose a novel multiple criteria group decision-making (MCDGM) method based on Dempster–Shafer theory (DST) and probabilistic linguistic term sets (PLTSs) to handle the distinctions between compensatory information at the criterion level and noncompensatory information at the individual level in the process of information fusion. Initially, the information at the individual level is extracted by BPA functions. Then, they are fused with DST considering ignorance and DMs’ reliabilities. Next, the obtained BPA functions are transformed into interval-valued PLTSs with the assistance of intermediate belief and plausibility. Subsequently, the interval-valued PLTSs are converted into standard PLTSs. After normalization, the holistic PLTS is obtained with weighted addition operation and the round function is applied to determine the ultimate evaluation result. Finally, a case simulation study of evaluating the marine ranching ecological security is presented to verify and improve the validity and feasibility of the proposed method and algorithm in practical application. The proposed method and its relevant algorithm are both innovative combination of DST and PLTSs from the perspective of compensatory and noncompensatory features of information, which provides a new angle of view for the development of probabilistic preference theory and is beneficial to apply probabilistic preference theory in practice.

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