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

Author

Listed:
  • Fei Teng

    (School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, China)

  • Peide Liu

    (School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, China)

  • Li Zhang

    (#x2020;School of Economic and Management, Shandong Yingcai University, Jinan, Shandong, China)

  • Juan Zhao

    (#x2020;School of Economic and Management, Shandong Yingcai University, Jinan, Shandong, China)

Abstract

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.

Suggested Citation

  • Fei Teng & Peide Liu & Li Zhang & Juan Zhao, 2019. "Multiple Attribute Decision-Making Methods with Unbalanced Linguistic Variables Based on Maclaurin Symmetric Mean Operators," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 105-146, January.
  • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:01:n:s0219622018500013
    DOI: 10.1142/S0219622018500013
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    References listed on IDEAS

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