A novel generalized grey target decision method with index and weight both containing mixed types of data

This is the authors' abstract. We don't add key points for this paper.

Purpose This paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data. Design/methodology/approach The decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option. Findings The certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method. Research limitations/implications The proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy. Originality/value The proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.