A Multi-criteria Picture Fuzzy Decision-making Model for Green Supplier Selection based on Fractional Programming

  • Guo Cao Northwestern Polytechnical University


Due to the increasing complexity in green supplier selection, there would be some important issues for expressing inherent uncertainty or imprecision of decision makers’ cognitive information in decision making process. As an extension of intuitionistic fuzzy sets (IFSs) and neutrosophic sets (NSs), picture fuzzy sets (PFSs) can better model and represent the hesitancy and uncertainty of decision makers’ preference information. In this study, an attempt has been made to present a multi-criteria picture fuzzy decision-making model for green supplier selection based on fractional programming. In this approach, the ratings of alternatives and weights of criteria are represented by PFSs and IFSs, respectively. Based on the available information, some pairs of fractional programming models are derived from the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the proposed biparametric picture fuzzy distance measure to determine the relative closeness coefficient intervals of green suppliers, which are aggregated for the criteria to generate the ranking order of all green suppliers by computing their optimal degrees of membership based on the ranking method of interval numbers. Finally, an example is conducted to validate the effectiveness of the proposed multi-criteria decision making (MCMD) method.


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How to Cite
CAO, Guo. A Multi-criteria Picture Fuzzy Decision-making Model for Green Supplier Selection based on Fractional Programming. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 1, feb. 2020. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1002>. Date accessed: 01 dec. 2020. doi: https://doi.org/10.15837/ijccc.2020.1.3762.


multi-criteria decision making (MCMD), picture fuzzy sets (PFSs), fractional programming, biparametric picture fuzzy distance measure, green supplier selection.