Abstract
To solve the problems related to inhomogeneous connections among the attributes, we introduce a novel multiple attribute group decision-making (MAGDM) method based on the introduced linguistic neutrosophic generalized weighted partitioned Bonferroni mean operator (LNGWPBM) for linguistic neutrosophic numbers (LNNs). First of all, inspired by the merits of the generalized partitioned Bonferroni mean (GPBM) operator and LNNs, we combine the GPBM operator and LNNs to propose the linguistic neutrosophic GPBM (LNGPBM) operator, which supposes that the relationships are heterogeneous among the attributes in MAGDM. Then, we discuss its desirable properties and some special cases. In addition, aimed at the different importance of each attribute, the weighted form of the LNGPBM operator is investigated, which we call the LNGWPBM operator. Then, we discuss some of its desirable properties and special examples accordingly. In the end, we propose a novel MAGDM method on the basis of the introduced LNGWPBM operator, and illustrate its validity and merit by comparing it with the existing methods.
Highlights
The goal of the multiple attribute group decision-making (MAGDM) method is to select the optimal scheme from finite alternatives
Dutta and Guha [48] proposed the partition Bonferroni mean (PBM) operator, where all attributes are sorted into several clusters, and the members have an inherent connection in the same clusters, but independence in different clusters
When |F2 | = 0, all arguments belong to the group F1, and are divided into t clusters; the proposed linguistic neutrosophic GPBM (LNGPBM) operator is simplified as the following form:
Summary
The goal of the multiple attribute group decision-making (MAGDM) method is to select the optimal scheme from finite alternatives. Our study is concentrated on how to use information aggregation operators to solve the MAGDM problems with LNNs. In addition, in real MAGDM problems, there are often homogeneous connections among the attributes. P2 = {G3 , G4 } meeting the condition where P1 and P2 have no relationship To solve this issue, Dutta and Guha [48] proposed the partition Bonferroni mean (PBM) operator, where all attributes are sorted into several clusters, and the members have an inherent connection in the same clusters, but independence in different clusters. In this paper, we are further focused on how to combine the GPBM operator with LNNs to address the MAGDM problems with heterogeneous relationships among attributes. Proposing a novel MAGDM method in light of the proposed LNGWPBM operator to address the MAGDM problems with LNNs and the heterogeneous relationships among its attributes.
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