期刊
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 29, 期 1, 页码 171-186出版社
IOS PRESS
DOI: 10.3233/IFS-151584
关键词
Dual Maclaurin symmetric mean (DMSM); uncertain linguistic variables; uncertain linguistic dual Maclaurin symmetric mean (ULDMSM) operator; uncertain linguistic weighted dual Maclaurin symmetric mean (ULWDMSM) operator; uncertain linguistic Choquet dual Maclaurin symmetric mean (ULCDMSM) operator; multiple attribute decision making
资金
- National Natural Science Foundation of China (NSFC) [71171048, 71371049]
- Ph.D. Program Foundation of Chinese Ministry of Education [20120092110038]
- Scientific Research and Innovation Project for College Graduates of Jiangsu Province [CXZZ13_0138]
- Scientific Research Foundation of Graduate School of Southeast University [YBJJ1454]
- Scholarship from China Scholarship Council [201406090096]
The Maclaurin symmetric mean (MSM) operator is a classical mean type aggregation operator used in modern information fusion theory, which is suitable to aggregate numerical values. The prominent characteristic of the MSM operator is that it can capture the interrelationship among the multi-input arguments. In this paper, we propose the dual Maclaurin symmetric mean (DMSM) operator and extend the DMSM operator to accommodate uncertain linguistic environment. Some new aggregation operators based on DMSM for aggregating uncertain linguistic information are developed, such as the uncertain linguistic dual Maclaurin symmetric mean (ULDMSM) operator, the uncertain linguistic weighted dual Maclaurin symmetric mean (ULWDMSM) operator and the uncertain linguistic Choquet dual Maclaurin symmetric mean (ULCDMSM) operator. Meanwhile, some desirable properties and special cases with respect to different parameter values of these operators are studied in detail. Furthermore, based on the ULWDMSM and ULCDMSM operators, two approaches to multiple attribute decision making with uncertain linguistic information are developed. Finally, a numerical example is provided to illustrate the feasibility of the proposed methods and deliver a comparative analysis with uncertain linguistic Bonferroni mean (ULBM) operator is also presented.
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