Maclaurin symmetric mean aggregation operators based on cubic Pythagorean linguistic fuzzy number

The Maclaurin symmetric mean (MSM) and dual Maclaurin symmetric mean (DMSM) operators are two aggregation operators to aggregate the cubic Pythagorean linguistic fuzzy number. The cubic Pythagorean linguistic fuzzy structure is more real to designate fuzzy data in real decision-making problems. The cubic Pythagorean linguistic fuzzy number is more superior and difficult information in the environment of the fuzzy set theory. We describe the score and accuracy function of CPLFN. We define some aggregation operators, including the CPLFAA, CGPLFAA, CPLFGA, CPLFMSM, and CPLFWMSM operators. We present some operators, with the CPLFDWMSMA, CPLFDOWMSMA, CPLFDHWMSMA, CPLFDWMSMG, CPLFDOWMSMG and CPLFDHWMSMG operators. Moreover, some properties and special cases of our proposed methods are also introduced. Then we present multi-attributive group decision-making based on proposed methods. Further, a numerical example is provided to illustrate the flexibility and accuracy of the proposed operators. Last, the proposed methods are compared with existing methods to examine the best developing skill initiatives.

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