A fuzzy multi-attribute decision-making method under risk with unknown attribute weights

Abstract This paper aims at solving hybrid multiple attributes decision-making problems under risk with attribute weight known and a new decision approach based on entropy weight and TOPSIS is proposed. First, the risk decision matrix is transformed into the certain decision matrix based on the expectation value. Then, the deviation entropy weight method is used to determine the attribute weights. And according to the definitions of the distance and the positive/negative ideal solutions for different data types, the relative closeness coefficients can be calculated by TOPSIS. Furthermore, the alternatives are ranked by the relative closeness coefficients. Finally, an application case is given to demonstrate the steps and effectiveness of the proposed approach.

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