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.

中文翻译：

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使用模糊多标准决策方法和人工神经网络进行绿色供应商选择


近年来，人们的环保意识大大增强，为了减少空气和水污染以及全球变暖等危害，应采用绿色采购。因此，在评估供应商时，应将其环境绩效与其他供应商选择标准一起考虑。提高生产和环境保护的可持续性意识对于整个环境和公司本身都非常重要，可以提高其竞争优势。而且，实现这一目标的步骤之一是公司尝试选择绿色供应商。因此，本研究的目的是提高人们的认识并解决绿色供应商选择的需求，并使用多标准决策模型详细阐述有关此问题的案例研究。一项调查是在一家制造公司进行的。对数据进行分析，并实施模糊 MCDM（多标准决策）方法和人工神经网络。模糊方法有模糊层次分析法（模糊AHP）、模糊TOPSIS 和模糊ELECTRE。 ANN从利润方面支持模糊MCDM模型的结果。 ANN 可以根据历史数据对当前年份做出最佳估计。模糊 MCDM 方法还将使用可用数据找到良好的解决方案，但由于存在不同的决策方法，因此会产生不同的解决方案。其目的是从此处获得的解决方案中产生协同作用，并产生更好的解决方案。与其使用单一方法，不如产生比所有方法提供的解决方案更好的解决方案。利用委员会模糊MCDM和ANN选择最佳绿色供应商获得了主导结果。