4.7 Article

Some power Maclaurin symmetric mean aggregation operators based on Pythagorean fuzzy linguistic numbers and their application to group decision making

期刊

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
卷 33, 期 9, 页码 1949-1985

出版社

WILEY
DOI: 10.1002/int.22005

关键词

multiple attribute group decision making; Pythagorean fuzzy linguistic set; power average operator; Maclaurin symmetric mean operator

资金

  1. National Natural Science Foundation of China [71471172, 71271124]
  2. Special Funds of Taishan Scholars Project of Shandong Province [ts201511045]
  3. Shandong Provincial Social Science Planning Project [15BGLJ06, 16CGLJ31, 16CKJJ27]
  4. Teaching Reform Research Project of Undergraduate Colleges and Universities in Shandong Province [2015Z057]
  5. Key research and development program of Shandong Province [2016GNC110016]
  6. Humanities and Social Sciences Research Project of Ministry of Education of China [17YJA630065, 17YJC630077]

向作者/读者索取更多资源

The power average (PA) operator and Maclaurin symmetric mean (MSM) operator are two important tools to handle the multiple attribute group decision-making (MAGDM) problems, and the combination of two operators can eliminate the influence of unreasonable information from biased decision makers (DMs) and can capture the interrelationship among any number of arguments. The Pythagorean fuzzy linguistic set (PFLS) is parallel to the intuitionistic linguistic set (ILS), which is more powerful to convey the uncertainty and ambiguity of the DMs than ILS. In this paper, we propose some power MSM aggregation operators for Pythagorean fuzzy linguistic information, such as Pythagorean fuzzy linguistic power MSM operator and Pythagorean fuzzy linguistic power weighted MSM (PFLPWMSM) operator. At the same time, we further discuss the properties and special cases of these operators. Then, we propose a new method to solve the MAGDM problems with Pythagorean fuzzy linguistic information based on the PFLPWMSM operator. Finally, some illustrative examples are utilized to show the effectiveness of the proposed method.

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