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Article type: Research Article
Authors: Fan, Jianping | Yan, Feng | Wu, Meiqin; *
Affiliations: School of Economics and Management, Shanxi University, Taiyuan, China
Correspondence: [*] Corresponding author. Meiqin Wu, School of Economics and Management, Shanxi University, Taiyuan, China. Tel.: +86 13834118599; E-mail: [email protected].
Abstract: In this article, the gained and lost dominance score (GLDS) method is extended into the 2-tuple linguistic neutrosophic environment, which also combined the power aggregation operator with the evaluation information to deal with the multi-attribute group decision-making problem. Since the power aggregation operator can eliminate the effects of extreme evaluating data from some experts with prejudice, this paper further proposes the 2-tuple linguistic neutrosophic numbers power-weighted average operator and 2-tuple linguistic neutrosophic numbers power-weighted geometric operator to aggregate the decision makers’ evaluation. Moreover, a model based on the score function and distance measure of 2-tuple linguistic neutrosophic numbers (2TLNNs) is developed to get the criteria weights. Combing the GLDS method with 2-tuple linguistic neutrosophic numbers and developing a 2TLNN-GLDS method for multiple attribute group decision making, it can express complex fuzzy information more conveniently in a qualitative environment and also consider the dominance relations between alternatives which can get more effective results in real decision-making problems. Finally, an applicable example of selecting the optimal low-carbon logistics park site is given. The comparing results show that the proposed method outperforms the other existing methods, as it can get more reasonable results than others and it is more convenient and effective to express uncertain information in solving realistic decision-making problems.
Keywords: Multiple attribute group decision making, 2-tuple linguistic neutrosophic numbers, power average operator, power geometric operator, the gained and lost dominance score method
DOI: 10.3233/JIFS-202748
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11523-11538, 2021
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