TOPSIS method based on q-rung orthopair picture fuzzy soft environment and its application in the context of green supply chain management

Sumbal Ali; Asad Ali; Ahmad Bin Azim; Abdul Samad Khan; Fuad A. Awwad; Emad A. A. Ismail; Sumbal Ali; Asad Ali; Ahmad Bin Azim; Abdul Samad Khan; Fuad A. Awwad; Emad A. A. Ismail

doi:10.3934/math.2024735

AIMS Mathematics

2024, Volume 9, Issue 6: 15149-15171. doi: 10.3934/math.2024735

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Research article

TOPSIS method based on q-rung orthopair picture fuzzy soft environment and its application in the context of green supply chain management

1.
Department of Mathematics & Statistics, Hazara University, Mansehra, 21300, Pakistan
2.
Research Centre for Computational Science, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, China
3.
Department of Quantitative analysis, College of Business Administration, King Saud University, P. O. Box 71115, Riyadh 11587, Saudi Arabia

Received: 17 February 2024 Revised: 03 April 2024 Accepted: 10 April 2024 Published: 26 April 2024
MSC : 60L70, 68N17

Green supplier selection has been an important technique for environmental sustainability and reducing the harm of ecosystems. In the current climate, green supply chain management (GSCM) is imperative for maintaining environmental compliance and commercial growth. To handle the change related to environmental concern and how the company manages and operates, they are integrated the GSCM into traditional supplier selection process. The main aims of this study were to outline both traditional and environmental criteria for selecting suppliers, providing a comprehensive framework to assist decision-maker in prioritizing green supplier effectively. In order to address issue to simulate decision-making problems and manage inaccurate data. A useful technique of fuzzy set was proposed to handle uncertainty in various real-life problems, but all types of data could not be handled such as incomplete and indeterminate. However, several extensions of fuzzy set were considered, such as intuitionistic fuzzy set, Pythagorean fuzzy set, q-rung orthopair fuzzy set, and q-rung orthopair fuzzy soft set considering membership and nonmember ship grade to handle the uncertainty problem. However, there was a lack of information about the neutral degree and parameterization axioms lifted by existing approaches, so to fill this gap and overcome the difficulties Ali et al. proposed a generalized structure by combining the structure of picture fuzzy set and q-rung orthopair fuzzy soft set, known as q-rung orthopair picture fuzzy soft sets, characterized by positive, neutral and negative membership degree with parameterization tools and aggregation operator to solve the multi criteria group decision-making problem. Additionally, the TOPSIS method is a widely utilized to assist individuals and organizations in selecting the most appropriate option from a range of choices, taking into account various criteria. Finally, we demonstrate an illustrative example related to GSCM to enhance competitiveness, based on criteria both in general and with a focus on environmental consideration, accompanied by an algorithm and flow chart.
- q-rung orthopair picture fuzzy soft numbers,
- TOPSIS technique,
- green supplier selection,
- MADM problem
Citation: Sumbal Ali, Asad Ali, Ahmad Bin Azim, Abdul Samad Khan, Fuad A. Awwad, Emad A. A. Ismail. TOPSIS method based on q-rung orthopair picture fuzzy soft environment and its application in the context of green supply chain management[J]. AIMS Mathematics, 2024, 9(6): 15149-15171. doi: 10.3934/math.2024735

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Abstract

Green supplier selection has been an important technique for environmental sustainability and reducing the harm of ecosystems. In the current climate, green supply chain management (GSCM) is imperative for maintaining environmental compliance and commercial growth. To handle the change related to environmental concern and how the company manages and operates, they are integrated the GSCM into traditional supplier selection process. The main aims of this study were to outline both traditional and environmental criteria for selecting suppliers, providing a comprehensive framework to assist decision-maker in prioritizing green supplier effectively. In order to address issue to simulate decision-making problems and manage inaccurate data. A useful technique of fuzzy set was proposed to handle uncertainty in various real-life problems, but all types of data could not be handled such as incomplete and indeterminate. However, several extensions of fuzzy set were considered, such as intuitionistic fuzzy set, Pythagorean fuzzy set, q-rung orthopair fuzzy set, and q-rung orthopair fuzzy soft set considering membership and nonmember ship grade to handle the uncertainty problem. However, there was a lack of information about the neutral degree and parameterization axioms lifted by existing approaches, so to fill this gap and overcome the difficulties Ali et al. proposed a generalized structure by combining the structure of picture fuzzy set and q-rung orthopair fuzzy soft set, known as q-rung orthopair picture fuzzy soft sets, characterized by positive, neutral and negative membership degree with parameterization tools and aggregation operator to solve the multi criteria group decision-making problem. Additionally, the TOPSIS method is a widely utilized to assist individuals and organizations in selecting the most appropriate option from a range of choices, taking into account various criteria. Finally, we demonstrate an illustrative example related to GSCM to enhance competitiveness, based on criteria both in general and with a focus on environmental consideration, accompanied by an algorithm and flow chart.

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