Green supplier evaluation and selection plays a crucial role in the green supply chain management of any organization to reduce the purchasing cost of materials and increase the flexibility and quality of products. To select the appropriate suppliers, many qualitative and quantitative criteria need to be considered in the decision process. Therefore, green supplier selection and evaluation can be considered as a multi-criteria decision making (MCDM) problem in vague environment.
Although many MCDM models have been proposed to evaluate and select suppliers. However, most existing fuzzy MCDM approaches has developed based on normal fuzzy numbers or converting from generalized fuzzy numbers into normal fuzzy numbers through normalization process. This leads to a restriction in the application of the fuzzy MCDM approaches. Therefore, this paper developed a new MCDM approach using generalized fuzzy numbers to select and segment suppliers. In the proposed generalized fuzzy MCDM approach, the ratings of alternatives and importance weights of criteria are expressed in linguistic terms using generalized fuzzy numbers. An application to a supplier selection and segmentation is presented.
Quality function deployment (QFD) recently has become a widely used quality management tool in product design and development. Various QFD approaches using crisp and fuzzy numbers have been presented in the literature. However, there exist few studies on the application of QFD technique under neutrosophic environment and no research has extended QFD for interval neutrosophic sets (INS) thus far. As a result, this study proposes a new integrated QFD-based INS for supporting the green supplier evaluation and selection process. In the proposed approach, the relative importance of the “WHATs,” the “HOWs”-“WHATs” correlation scores, the resulting weights of the “HOWs,” and the impact of each potential green supplier are assessed in INS. The technique for order performance by similarity to ideal solution (TOPSIS) is developed based on INS to obtain the final ranking of alternatives. A case study is further used to illustrate the computational procedure of the proposed approach.
Nowadays, the TOPSIS method is one of the well-known methods for classical MCDM. Many TOPSIS methods have been proposed to solve the real life problem using fuzzy sets and an extension of the fuzzy sets (bipolar fuzzy sets, intuitionistic fuzzy sets, neutrosophic sets, bipolar neutrosophic sets). It seems that no study has developed the TOPSIS method under interval bipolar linguistic neutrosophic environments. Therefore, this study proposes a new TOPSIS method using interval bipolar linguistic neutrosophic set (IBLNS) to evaluate and select the green supplier. In the propose method, some set theoretic operations, such as union, intersection and complement and the operational rules of IBLNS-TOPSIS are defined. Then, the TOPSIS procedure in IBLNS and an application to a green supplier selection are presented.

Green supplier evaluation and selection plays a crucial role in the green supply chain management of any organization to reduce the purchasing cost of materials and increase the flexibility and quality of products. To select the appropriate suppliers, many qualitative and quantitative criteria need to be considered in the decision process. Therefore, green supplier selection and evaluation can be considered as a multi-criteria decision making (MCDM) problem in vague environment.
Although many MCDM models have been proposed to evaluate and select suppliers. However, most existing fuzzy MCDM approaches has developed based on normal fuzzy numbers or converting from generalized fuzzy numbers into normal fuzzy numbers through normalization process. This leads to a restriction in the application of the fuzzy MCDM approaches. Therefore, this paper developed a new MCDM approach using generalized fuzzy numbers to select and segment suppliers. In the proposed generalized fuzzy MCDM approach, the ratings of alternatives and importance weights of criteria are expressed in linguistic terms using generalized fuzzy numbers. An application to a supplier selection and segmentation is presented.
Quality function deployment (QFD) recently has become a widely used quality management tool in product design and development. Various QFD approaches using crisp and fuzzy numbers have been presented in the literature. However, there exist few studies on the application of QFD technique under neutrosophic environment and no research has extended QFD for interval neutrosophic sets (INS) thus far. As a result, this study proposes a new integrated QFD-based INS for supporting the green supplier evaluation and selection process. In the proposed approach, the relative importance of the “WHATs,” the “HOWs”-“WHATs” correlation scores, the resulting weights of the “HOWs,” and the impact of each potential green supplier are assessed in INS. The technique for order performance by similarity to ideal solution (TOPSIS) is developed based on INS to obtain the final ranking of alternatives. A case study is further used to illustrate the computational procedure of the proposed approach.
Nowadays, the TOPSIS method is one of the well-known methods for classical MCDM. Many TOPSIS methods have been proposed to solve the real life problem using fuzzy sets and an extension of the fuzzy sets (bipolar fuzzy sets, intuitionistic fuzzy sets, neutrosophic sets, bipolar neutrosophic sets). It seems that no study has developed the TOPSIS method under interval bipolar linguistic neutrosophic environments. Therefore, this study proposes a new TOPSIS method using interval bipolar linguistic neutrosophic set (IBLNS) to evaluate and select the green supplier. In the propose method, some set theoretic operations, such as union, intersection and complement and the operational rules of IBLNS-TOPSIS are defined. Then, the TOPSIS procedure in IBLNS and an application to a green supplier selection are presented.

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