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A multi‐criteria sustainable supplier selection framework based on neutrosophic fuzzy data and entropy weighting
Author(s) -
Zeng Shouzhen,
Hu Yingjie,
Balezentis Tomas,
Streimikiene Dalia
Publication year - 2020
Publication title -
sustainable development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.115
H-Index - 64
eISSN - 1099-1719
pISSN - 0968-0802
DOI - 10.1002/sd.2096
Subject(s) - weighting , computer science , entropy (arrow of time) , data mining , fuzzy logic , selection (genetic algorithm) , similarity measure , measure (data warehouse) , artificial intelligence , quantum mechanics , medicine , physics , radiology
The gains in corporate sustainability are important for business development under the increasing competition and climate change. The importance of the green and sustainable supplier selection has increased due to the environmental concerns and regulations. The information for business decisions is often vague and imprecise. Therefore, this paper develops a fuzzy methodology for sustainable supplier selection based on fuzzy information. Single‐valued neutrosophic set (SVNS) is a very popular tool for processing potentially uncertain information provided by decision‐makers. Thus, SVNS is considered as a useful extension of the existing methods in uncertain complex situations. In order to facilitate the multi‐attribute decision‐making (MADM), this paper develops a new similarity measure for the SVNSs and explores its application possibilities. For achieving the aim, a single‐valued neutrosophic (SVN) hybrid weighted similarity (SVNHWS) measure is presented to reflect degree of similarity of SVNSs more effectively. Moreover, a method based on the SVNHWS and entropy measures is constructed for handling SVN MADM problems, in which, the entropy measure is used to derive the unknown weights information of attributes. Finally, an illustrative mathematical example dealing with the sustainable supplier selection is provided. The reliability of the proposed technique is tested by means of the comparative analysis.