Green Supplier Selection Using Fuzzy Multiple-Criteria Decision-Making Methods and Artificial Neural Networks

• Published in: Computational intelligence and neuroscience Vol. 2020; no. 2020; pp. 1 - 26
• Main Authors: Gegovska, Tina, Cakar, Tarik, Koker, Rasit
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Air pollution; Analytic hierarchy process; Artificial neural networks; Case studies; Climate change; Commerce; Component and supplier management; Decision analysis; Decision Making; Environmental awareness; Environmental management; Environmental performance; Environmental protection; Fuzzy Logic; Global warming; Green procurement; Multiple criterion; Neural networks; Neural Networks, Computer; Suppliers; Supply chain management; Sustainable development; Vendor relations; Water pollution
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2020/8811834

In recent years, environmental awareness has increased considerably, and in order to decrease endangerments such as air and water pollution, and also global warming, green procurement should be employed. Therefore, in the assessment of suppliers, their environmental performance should be taken into consideration along with other criteria for supplier selection. Raising awareness of sustainability in production and conservation and protection of the environment is very important both for the whole environment and for the company itself by increasing its competitive advantage. And, one of the steps to achieve this is for the companies to try to select green suppliers. So, the purpose of this study is to raise awareness and tackle the need for green supplier selection and, using multiple-criteria decision-making models, to elaborate a case study regarding this. A survey was conducted in a manufacturing firm. The data were analysed, and fuzzy MCDM (multicriteria decision-making) methods and artificial neural networks were implemented. Fuzzy methods are the fuzzy analytic hierarchy process (fuzzy AHP), fuzzy TOPSIS, and fuzzy ELECTRE. ANN supports the result of fuzzy MCDM models from the profit side. ANN can make the best estimate of the current year based on historical data. Fuzzy MCDM methods will also find good solutions using the available data but will produce different solutions as there are different decision-making methods. It is aimed to produce a synergy from the solutions obtained here and to produce a better solution. Instead of a single method, it would be more accurate to produce a better solution than the solution provided by all of them. The dominant result has been obtained using the committee fuzzy MCDM and ANN to select the best green supplier.