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Enhancing green supplier selection: A nonlinear programming method with TOPSIS in cubic Pythagorean fuzzy contexts
Musa Khan1, Wu Chao1, Muhammad Rahim2
1School of Finance and Economics, Jiangsu University, Zhenjiang, Jiangsu, P. R. China.
Plos One
|December 5, 2024
Summary
This study introduces a new nonlinear programming approach using cubic Pythagorean fuzzy sets to select the best green suppliers. It effectively integrates advanced technologies with environmental criteria for intelligent decision-making.
Area of Science:
- Operations Research
- Information Technology
- Environmental Management
Background:
- Information and communication technologies (ICT) like AI and IoT are transforming production systems into intelligent ones.
- Integrating ICT with environmental criteria is crucial for modern supplier selection.
- Existing methods struggle to represent complex fuzzy information effectively.
Purpose of the Study:
- To develop a novel Nonlinear Programming (NLP) approach for green supplier selection.
- To utilize cubic Pythagorean fuzzy sets (CPFS) for enhanced information representation in decision-making.
- To address limitations of existing methods using interval-valued PFS (IVPFS) and PFS.
Main Methods:
- A Nonlinear Programming (NLP) model is proposed.
- The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is integrated.
- Cubic Pythagorean fuzzy sets (CPFS) are employed to handle uncertainty and imprecision.
Main Results:
- The proposed NLP-TOPSIS method effectively identifies optimal green suppliers.
- CPFS successfully integrates both interval-valued PFS and PFS information.
- The methodology demonstrates accuracy and effectiveness through a case study.
Conclusions:
- The novel approach offers a robust framework for green supplier selection in intelligent environments.
- CPFS provides a superior method for representing complex decision-making information.
- This research contributes to sustainable supply chain management through advanced decision support systems.
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