An Extended VIKOR Method for Multiple Attribute Decision Making with Linguistic D Numbers Based on Fuzzy Entropy

Peide Liu, Xiaohong Zhang and Zhanyou Wang
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Peide Liu: School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, Shandong, China
Xiaohong Zhang: School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, Shandong, China
Zhanyou Wang: #x2020;Research Department, Shandong Management University, Jinan 250100, Shandong, China

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 01, 143-167

Abstract: The linguistic D numbers (LDNs) can express the fuzzy evaluation information more easily and precisely by combining the advantages of linguistic terms (LTs) and D numbers (DNs). Existing researches on the fuzzy entropy of LDNs are rare, and most of the definitions of fuzzy entropy for LDNs are unreasonable. In view of this research gap, this paper improves the definition of fuzzy entropy of LDNs, which simultaneously considers the effects of confidence degrees and LTs on the value of fuzzy entropy in LDNs. Then, the weights of attributes can be calculated by the improved fuzzy entropy. Further, a new combination rule for LDNs is also presented in this paper. Based on these studies, we extend the traditional Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to the LDNs and develop the LD-VIKOR method. The proposed LD-VIKOR method is convenient to handle MADM problems with unknown attributes weights under the environment of LDNs. Finally, we verify the validity and reliability of the proposed method by an illustrative example, and analyze the advantages of the proposed method by comparing it with other existing MADM methods.

Keywords: Multiple attribute decision making (MADM); linguistic D numbers (LDNs); VIKOR method; fuzzy entropy (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622019500433

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