Determining the Most Efficient Supplier Considering Imprecise Data in Data Envelopment Analysis (DEA), a extension for Toloo and Nalchigar's model

Supplier selection in supply chain as a multi-criteria decision making problem (containing both qualitative and quantitative criteria) is one of the main factors in a successful supply chain. To this purpose, Toloo and Nalchigar (2011) proposed an integrated data envelopment analysis (DEA) model to find the most efficient (best) supplier by considering imprecise data. In this paper, it will be shown that their model randomly selects an efficient supplier as the most efficient and therefore their model cannot find the most efficient supplier correctly. We also explain some other problems in this model and propose a modified model to resolve the drawbacks. The proposed model in this paper finds the most efficient supplier considering imprecise data by solving only one mixed integer linear programming. In addition, a new algorithm is proposed for determining and ranking other efficient suppliers. Afficiency of the proposed approach is explained by considering imprecise data for 18 suppliers.