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Green Supplier Selection Using Fuzzy Multiple-Criteria Decision-Making Methods and Artificial Neural Networks
Tina Gegovska1, Rasit Koker2, Tarik Cakar3
1International Balkan University, Engineering Faculty, Industrial Engineering Dept., Campus Str. Makedonsko Kosovska Brigada bb, Skopje 1000, North Macedonia.
Green procurement is essential for environmental protection. This study uses fuzzy multicriteria decision-making (MCDM) methods and artificial neural networks (ANN) to identify the best green suppliers, enhancing sustainability.
Area of Science:
- Environmental Science
- Operations Research
- Business Management
Background:
- Increasing environmental awareness necessitates sustainable business practices.
- Green procurement is crucial for mitigating pollution and global warming.
- Supplier environmental performance is a key factor in competitive advantage.
Purpose of the Study:
- To highlight the importance of green supplier selection.
- To address the need for effective green supplier selection methods.
- To develop a case study using multicriteria decision-making (MCDM) models.
Main Methods:
- A survey was conducted in a manufacturing firm.
- Fuzzy MCDM methods including fuzzy analytic hierarchy process (AHP), fuzzy TOPSIS, and fuzzy ELECTRE were implemented.
- Artificial neural networks (ANN) were used to support fuzzy MCDM results and provide profit-side estimations.
Main Results:
- Fuzzy MCDM methods identified potential green suppliers.
- ANN provided profit-based estimations using historical data.
- A synergistic approach combining committee fuzzy MCDM and ANN yielded the optimal selection of green suppliers.
Conclusions:
- Integrating fuzzy MCDM and ANN offers a robust framework for green supplier selection.
- A combined approach provides more accurate and reliable results than single methods.
- This study demonstrates a practical application for enhancing corporate environmental responsibility through strategic procurement.
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