4.6 Article

TOPSIS Method Based on Correlation Coefficient under Pythagorean Fuzzy Soft Environment and Its Application towards Green Supply Chain Management

Journal

SUSTAINABILITY
Volume 13, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/su13041642

Keywords

pythagorean fuzzy sets; pythagorean fuzzy soft sets; green supply chain management; TOPSIS

Funding

  1. National Natural Science Foundation of China [11971384]

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This paper mainly discusses the concept and characteristics of correlation coefficients and weighted correlation coefficients in Pythagorean fuzzy soft sets, introduces correlation operators and the TOPSIS technique based on correlation coefficients, develops a decision-making technique, and applies TOPSIS in green supplier selection within green supply chain management. The practicality, efficacy, and flexibility of the proposed approach are proven through comparative analyses.
The correlation coefficient between two variables is an important aspect of statistics. The accuracy of assessments of correlation relies on information from a set of discourses. Data collected in statistical studies are often full of exceptions. Pythagorean fuzzy soft sets (PFSS) are a parametrized family of extended Pythagorean fuzzy sets (PFS). They comprise a generalization of intuitionistic fuzzy soft sets which may be used to accurately assess deficiencies and uncertainties in evaluations. PFSS can accommodate uncertainty more competently than intuitionistic fuzzy soft sets and are the most important strategy when dealing with fuzzy information in decision-making processes. Herein, the concept and characteristics of correlation coefficients and the weighted correlation coefficients in PFSS are discussed. We also introduce the Pythagorean fuzzy soft weighted average (PFSWA) and Pythagorean fuzzy soft weighted geometric (PFSWG) operators and discuss their desirable characteristics. A prioritization technique for order preference by similarity to the ideal solution (TOPSIS) under the PFSS environment based on correlation coefficients and weighted correlation coefficients will be introduced. Through the proposed methodology, a technique for decision-making is developed. Additionally, an application of the proposed TOPSIS technique is presented for green supplier selection in green supply chain management (GSCM). The practicality, efficacy, and flexibility of the proposed approach is proved through comparative analyses, drawing upon existing studies.

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