Kullback-Leibler Distance Based Generalized Grey Target Decision Method With Index and Weight Both Containing Mixed Attribute Values

This paper proposes a generalized grey target decision method (GGTDM) with index and weight both containing mixed attribute values based on Kullback-Leibler (K-L) distance. The proposed approach builds the weight function converting the mixed attribute-based weights into the certain number-based weights and takes the comprehensive weighted K-L distance as the decision-making basis (DMB). The proposed approach conducts its task in the following steps. First, all indices of alternatives are converted into binary connection numbers. Second, the two-tuple (determinacy, uncertainty) numbers originated from index binary connection numbers are obtained. Third, the two-tuple (determinacy, uncertainty) numbers of target center are calculated. Following that the certain number-based weights are obtained by the weight function. Then the comprehensive weighted K-L distance of each alternative and its target center is calculated. And the final decision making is based on the value of comprehensive weighted K-L distance with which the smaller the better. A case study illustrates the proposed approach with its effectiveness of converting the uncertain weights into the certain weights and the accurate results comparing with other decision-making methods.