Data-driven multi-attribute decision-making by combining probability distributions based on compatibility and entropy

Multi-attribute decision-making has many applications in different fields. How to make decisions objectively when there are many attributes is still an open issue. This paper proposes a data-driven multi-attribute decision-making method considering the compatibility and entropy. Mainly, data of different decision attributes are normalized to probability distributions. The compatibility weight and entropy weight are computed respectively and then combined to a final weight. The scores of decision objects are derived by combining weighted probability distributions. In order to verify the effectiveness of the proposed method, two examples are given to compare with the AHP method and an improved data envelopment analysis method respectively. The former results show that the proposed method can obtain more objective results and produce a low computation complexity. The latter demonstrate the proposed method focuses more on the overall performance of decision attributes while the improved data envelopment analysis emphasises more on the ecological performance.