Multiple Attribute Decision-Making Methods with Unbalanced Linguistic Variables Based on Maclaurin Symmetric Mean Operators
Fei Teng,
Peide Liu,
Li Zhang and
Juan Zhao
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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
International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 01, 105-146
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.
Keywords: Unbalanced linguistic term set; Maclaurin symmetric mean; multiple attribute decision-making (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:18:y:2019:i:01:n:s0219622018500013
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DOI: 10.1142/S0219622018500013
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