A Multi objective model for supplier evaluation and selection in the presence of both cardinal and imprecise data

Abstract

Imprecise data envelopment analysis (IDEA) has been applied for supplier selection in the presence of both cardinal and imprecise data. In addition to its popularity, IDEA has some drawbacks such as unrealistic inputs-outputs weights and poor discrimination power among all DMUs. To alleviate these deficiencies, this paper develops a multi objective imprecise data envelopment analysis (MOIDEA) based on the common weights. The proposed MOIDEA model is utilized for supplier evaluation and selection in the case where there exist both cardinal and imprecise data. To show both robustness and discriminating power of the proposed approach, it is applied on a numerical example taken from the literature. The results reveal several merits of the common weight MOIDEA model for supplier selection.