Research On 2-tuple Linguistic Aggregation Operators For Multi-attribute Group Decision-making

As an important branch of the decision-making theory,multi-attribute group decision-making plays a pivotal role in various fields.Due to the complexities of decision-making environment and the limitations of human knowledge,decision-makers cannot accurately express their opinions in the form of a single linguistic variable or a numerical value in the process of decision-making.The emergence of 2-tuple linguistic variable can effectively solve the problems mentioned above.Under the cumbersome and interactive decision-making environment,aggregation operators can reasonably integrate the interactions among the decision information into the decision-making process to realize information fusion.The underlying objective of this study is to address information fusion under complex decision-making situations.The aggregation operators are developed within context of grey 2-tuple linguistic variables,hesitant 2-tuple linguistic variables and hesitant Picture 2-tuple linguistic variables,respectively.The main contents of this paper are outlined below:(1)To develop the way of the information fusion with incomplete attribute information under the environment of the grey 2-tuple linguistic variable,grey 2-tuple linguistic prioritized averaging operator and grey 2-tuple linguistic generalized dual Bonferroni mean operator have been conducted,and their properties and some special cases are discussed.Simultaneously,according to the grey relationship analysis method,the linear programming model is employed to extract attribute weights from partially known attribute information in the process of decision-making,which is combined with aggregation operators to design a multi-attribute group decision making method,and the feasibility and flexibility of the proposed method are verified by analyzing of Brent oil procurement case with the detailed comparative discussion.(2)To address the fusion of multiple decision-makers information under the situation of hesitant 2-tuple linguistie variable,hesitant 2-tuple linguistic Bonferroni mean operator and hesitant 2-tuple linguistic prioritized weighted Bonferroni mean operator are defined,by which the group opinion can be fused.At the same time,the possibility degree between hesitant 2-tuple linguistic variables is proposed,which is integrated into the classical TODIM method to characterize the risk aversion attitude of individuals decision-makers and fuse the attribute information in the process of decision-making.Based on the defined operators and the improved TODIM method,a multi-attribute group decision-making method is offered to solve enterprise supplier selection,and a comparative analysis with other pertinent methods is carried out to illustrate the rationality of designed method.(3)In order to realize the information fusion under the environment of hesitant Picture 2-tuple linguistic variable,the Archimedean t-norm and t-conorm are extended to the context of hesitant Picture 2-tuple linguistic variable,and some operational principles of hesitant Picture 2-tuple linguistic variables are initiated.Based on the proposed operational principles,hesitant Picture 2-tuple linguistic weighted averaging operators and geometric operator based on the Archimedean t-norm and t-conorm are defined by embedding the Archimedean t-norm and t-conorm.Subsequently,a multi-attribute group decision-making method is established with the aid of the hesitant Picture 2-tuple linguistic aggregation operators.Moreover,an experimental study of selecting service outsourcing supplier is reported to demonstrate the feasibility and practicality of established method.