Affiliations:
Department of Information Engineering, Chongqing Youth Vocational and Technical College, Chongqing, China
Correspondence:
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Corresponding author. Ge Zhou, Department of Information Engineering, Chongqing Youth Vocational and Technical College, Chongqing, China. Tel./Fax: +86 023 68312931; E-mail: 4096185@qq.com.
Abstract: From the beginning of 21th, enterprise’s competition predominance depends on capability of green supply chain. Suppliers are fountains of green supply chain, and the performance will directly influence the whole supply chain performance. The green supplier performance management is a key and difficult question in green supply chain manage system, which contains two cores, supplier performance evaluation and performance improvement. With the practice of two items, enterprise can control supplier performance continuously, and take improve actions in time, make the supplier performance to achieve requested level. Finally, enterprise can optimize the competition capability. In this paper, we investigate the multiple attribute decision making (MADM) problems for supplier performance evaluation with triangular fuzzy information. Motivated by the idea of Bonferroni mean and Einstein operations, we develop the aggregation techniques called the triangular fuzzy Einstein Bonferroni mean (TFEBM) operator for aggregating the triangular fuzzy information. For the situations where the input arguments have different importance, we then define the triangular fuzzy weighted Einstein Bonferroni mean (TFWEBM) operator, based on which we develop the procedure for multiple attribute decision making under the triangular fuzzy environments. Finally, a practical example for supplier performance evaluation is given to verify the developed approach.
Keywords: Multiple attribute decision making (MADM), triangular fuzzy numbers, Bonferroni mean, triangular fuzzy Bonferroni mean (TFBM) operator, triangular fuzzy weighted Bonferroni mean (TFWBM) operator, Chinese traditional medicine industry, modernization development
