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Improved fuzzy evidential DEMATEL method based on two-dimensional correlation coefficient and negation evidence

  • Fuzzy systems and their mathematics
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Abstract

The Decision-making Trial and Evaluation Laboratory (DEMATEL) has widespread application in many fields as a system analysis method to explain the relationship between the risk factors in a system. By analyzing the influence relationship and degree of influence among risk factors, DEMATEL can determine their importance and priority. One characteristic of DEMATEL is that expert experience and knowledge should be fully considered. However, in practical applications, there are great uncertainties in the evaluation process because of the differences in experts’ historical experiences and subjective opinions. To address this issue, an improved fuzzy evidential DEMATEL method based on the two-dimensional correlation coefficient (2-DCC) and negation of basic probability assignment (BPA) is proposed in this paper. The new method uses 2-DCC to calculate the correlation between different expert evaluations in horizontal and vertical directions to get an overall correlation r and converts it into the macro-credibility and weight of experts. Then, to construct BPA according to the fuzzy evaluations, the total uncertainty (TU) measure and negation of BPA under the framework of evidence theory are used to deal with the uncertainty of evaluation, and then, the evaluations will be weighted and fused. Finally, the DEMATEL method is used to calculate the comprehensive influence matrix, and the importance of each risk factor is calculated. Two applications well verified the effectiveness of our method.

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Funding

The work was supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2023-JC-QN-0689) and NWPU Research Fund for Young Scholars (Grant No. G2022WD01010).

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Correspondence to Yongchuan Tang.

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Liu, Y., Tang, Y., Yang, Z. et al. Improved fuzzy evidential DEMATEL method based on two-dimensional correlation coefficient and negation evidence. Soft Comput 27, 11177–11192 (2023). https://doi.org/10.1007/s00500-023-08748-y

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