Approaches to multiple attribute decision making based on picture 2-tuple linguistic power Hamy mean aggregation operators

In this paper, the multiple attribute decision making (MADM) problems are investigated with picture 2-tuple linguistic information. Then, based on Hamy mean (HM) operator and dual Hamy mean (DHM) operator, the power average and power geometric operations are utilized to develop some picture 2-tuple linguistic power Hamy mean aggregation operators: picture 2-tuple linguistic power weighted Hamy mean (P2TLPWHM) operator, picture 2-tuple linguistic power weighted dual Hamy mean (P2TLPWDHM) operator, picture 2-tuple linguistic power ordered weighted Hamy mean (P2TLPOWHM) operator, picture 2-tuple linguistic power ordered weighted dual Hamy mean (P2TLPOWDHM) operator, picture 2-tuple linguistic power hybrid Hamy mean (P2TLPHHM) operator and picture 2-tuple linguistic power hybrid dual Hamy mean (P2TLPHDHM) operator. The prominent characteristic of these proposed operators are studied. Then, these operators are utilized to develop some approaches to solve the picture 2-tuple linguistic multiple attribute decision making problems. Finally, the proposed method is demonstrated through a practical example for enterprise resource planning (ERP) system selection of how the proposed methods help us and is effective in MADM problems.

Automated summary

This paper investigates the use of picture 2-tuple linguistic information to address multiple attribute decision making problems, proposing various picture 2-tuple linguistic power Hamy mean aggregation operators. The effectiveness of these operators is demonstrated through a practical example in enterprise resource planning system selection.