Abstract
In respect to the multi-attribute group decision making (MAGDM) problems in which the evaluated value of each attribute is in the form of q-rung orthopair fuzzy numbers (q-ROFNs), a new approach of MAGDM is developed. Firstly, a new aggregation operator, called the partitioned Maclaurin symmetric mean (PMSM) operator, is proposed to deal with the situations where the attributes are partitioned into different parts and there are interrelationships among multiple attributes in same part whereas the attributes in different parts are not related. Some desirable properties of PMSM are investigated. Then, in order to aggregate the q-rung orthopair fuzzy information, the PMSM is extended to q-rung orthopair fuzzy sets (q-ROFSs) and two q-rung orthopair fuzzy partitioned Maclaurin symmetric mean (q-ROFPMSM) operators are developed. To eliminate the negative influence of unreasonable evaluation values of attributes on aggregated result, we further propose two q-rung orthopair fuzzy power partitioned Maclaurin symmetric mean (q-ROFPPMSM) operators, which combine the PMSM with the power average (PA) operator within q-ROFSs. Finally, a numerical instance is provided to illustrate the proposed approach and a comparative analysis is conducted to demonstrate the advantage of the proposed approach.
Highlights
Multi-attribute group decision making (MAGDM) is one of the most important branches of modern decision making theory
As the complexity of MAGDM problems increase, we may encounter the following case: the decision maker maybe evaluate the attributes in form of q-rung orthopair fuzzy number (q-ROFN) and provide some unduly high or unduly low assessments owing to time shortage and a lack of priori experience
We propose q-ROFPPMSM and q-ROFWPPMSM based on the power average (PA) and partitioned Maclaurin symmetric mean (PMSM) operators
Summary
Multi-attribute group decision making (MAGDM) is one of the most important branches of modern decision making theory. As the complexity of MAGDM problems increase, we may encounter the following case: the decision maker maybe evaluate the attributes in form of q-rung orthopair fuzzy number (q-ROFN) and provide some unduly high or unduly low assessments owing to time shortage and a lack of priori experience. These unreasonable assessments may negatively affect the decision results. In order to reduce the negative influence of unreasonable assessments on decision result, we take advantage of PMSM and PA and propose q-ROFPPMSM and the weighted form of q-ROFPPMSM, which is called q-rung orthopair fuzzy weighted power partitioned Maclaurin symmetric mean (q-ROFWPPMSM) operator.
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