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

Author

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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)

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.

Suggested Citation

  • Peide Liu & Xiaohong Zhang & Zhanyou Wang, 2020. "An Extended VIKOR Method for Multiple Attribute Decision Making with Linguistic D Numbers Based on Fuzzy Entropy," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 143-167, January.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:01:n:s0219622019500433
    DOI: 10.1142/S0219622019500433
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    References listed on IDEAS

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    1. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.
    2. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    3. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
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