Double quantitative fuzzy rough set-based improved AHP method and application to supplier selection decision making - Hu, Xiaoyuan; Sun, Bingzhen; Chen, Xiangtang (International journal of machine learning and cybernetics, 2020)
Selecting excellent supplier is the foundation of establishing efficient supply chain. This paper presents a novel hybrid model for supplier selection decision making problem by combining double quantitative fuzzy rough set and analytic hierarchy process ( AHP ), namely F u z z y - A H P . we first transform the supplier decision-making into a rough-approximation problem in the double quantization decision approximation space with fuzzy decision objects, and then construct a new supplier selection decision model and method based on double-quantitative fuzzy rough sets. Then, the double quantitative fuzzy rough set is utilized to calculate the upper and lower approximations of the fuzzy decision object in the quantitative approximation space. Furthermore, the lower and upper approximations are applied to establish the pairwise comparison matrix and analytic hierarchy process is employed to rank these suppliers comprehensively. Finally, an experiment study with six ERP bidder in an Indian mining behemoth is carried out in this paper. The results reveal that the proposed technique can select effective suppliers, but also realize a comprehensive ranking. This research has enriched the methodology of supplier evaluation and selection, as well as owns theoretical value in exploring the coordinated development of supply chain to some extent.