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Abstract
This paper provides a new multi-attributes decision making approach on the basis of the Gini OWA ( $\text{G}^{\mathrm {p,q}}$ -OWA) operator, in which the Gini operator is the combination of the Gini mean and the OWA operator. Desired properties and several generalized forms of the $\text{G}^{\mathrm {p,q}}$ -OWA operator are investigated. In order to determine the $\text{G}^{\mathrm {p,q}}$ -OWA operator weights, an orness measure is proposed, and a generalized least squares deviation model (GLSD) is put forward. Finally, a case study of low carbon supplier selection is shown to illustrate the effectiveness and practicality of the proposed method.
Highlights
In recent years, public health and economic development mode have been increasingly affected by carbon emissions
Low carbon supplier selection (LCSS), one of the most crucial activities in low carbon supply chain management, can be achieved by using the multiple attributes decision making (MADM), which is to select the best supplier(s) from a set of suppliers based on a set of attributes
The purpose of this paper is to introduce a novel approach to LCSS based on the Gini ordered weighted averaging (OWA) (Gp,q-OWA) operator which is a generalization of the generalized OWA (GOWA) operator that utilizes the Gini mean [44] in the OWA operator with parameters p and q
Summary
Public health and economic development mode have been increasingly affected by carbon emissions. One critical issue in LCSS with MADM is how to choose the aggregation technique, which directly affects the group consensus [2]–[4] He and He [5] provided an important aggregation operator called the ordered weighted averaging (OWA) operator, which is an useful technique for information fusion. One can find the other generalizations of OWA operator in [23]–[34] Another key issue among the applications of the OWA operator in decision making process is how to determine the associated weights. P. Xiao et al.: Approach for Multi-Attribute Decision Making Based on Gini Aggregation Operator operator and weighting vector.
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