Abstract
The social capital selection of a public–private-partnership (PPP) project could be regarded as a classical multiple attribute group decision-making (MAGDM) issue. In this paper, based on the traditional gained and lost dominance score (GLDS) method, the q-rung orthopair fuzzy entropy-based GLDS method was used to solve MAGDM problems. First, some basic theories related to the q-rung orthopair fuzzy sets (q-ROFSs) are briefly reviewed. Then, to fuse the q-rung orthopair fuzzy information effectively, the q-rung orthopair fuzzy Hamacher weighting average (q-ROFHWA) operator and q-rung orthopair fuzzy Hamacher weighting geometric (q-ROFHWG) operator based on the Hamacher operation laws are proposed. Moreover, to determine the attribute weights, the q-rung orthopair fuzzy entropy (q-ROFE) is proposed and some significant merits of it are discussed. Next, based on the q-ROFHWA operator, q-ROFE, and the traditional GLDS method, a MAGDM model with q-rung orthopair fuzzy information is built. In the end, a numerical example for social capital selection of PPP projects is provided to testify the proposed method and deliver a comparative analysis.
Highlights
In actual decision-making applications, how to choose the most desirable alternative from a given alternative set is very important [1,2,3]
The previous works ranked all alternatives by the score results but failed to reflect the dominance flow of the alternatives over the attributes; in this study, we proposed the q-rung orthopair fuzzy entropy-based gained and lost dominance score (GLDS) method for multiple attribute group decision-making (MAGDM) issues, which can overcome this limitation
To further verify the effective and scientific nature of our proposed approach, in this part, we shall compare the q-rung orthopair fuzzy entropy-based GLDS method with other existing methods, such as the q-ROFWA and q-ROFWG operators presented by Liu and Wang [21] and the q-rung orthopair fuzzy cosine similarity measures given in Wang et al [62]
Summary
In actual decision-making applications, how to choose the most desirable alternative from a given alternative set is very important [1,2,3]. Provided some Heronian mean operators to aggregate the q-ROFSs. Wang et al [31] defined the multi-attributive border approximation area comparison (MABAC) method for multiple attribute group decision-making (MAGDM) using q-ROFSs. the above-mentioned methods can only rank all alternatives using the score results and failed to reflect the dominance flow of the alternatives over the attributes; on account of this, Wu and. Motivated by them, we extended the GLDS method to q-ROFSs and built a novel q-rung orthopair fuzzy GLDS decision-making model in this study. The previous works ranked all alternatives by the score results but failed to reflect the dominance flow of the alternatives over the attributes; in this study, we proposed the q-rung orthopair fuzzy entropy-based GLDS method for MAGDM issues, which can overcome this limitation.
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