Intuitionistic fuzzy multi-attribute decision-making method based on improved score function

Abstract

Aiming at the problem that attribute weight information in the classic TOPSIS algorithm is given by experts and is too subjective due to personal preference, a method for determining attribute weight based on improved score function is proposed, which allows experts to give the importance information of attributes through intuitionistic fuzzy numbers rather than accurate values, and increases the flexibility of decision assignment. The related properties of the improved score function are discussed and compared with other score functions to verify the rationality of the score function. Combined with intuitionistic fuzzy mixed average operator, the evaluation information of each expert is integrated from the perspective of group experts, and the attribute weight is obtained by using the normalization principle to process the score value. An improved TOPSIS method for multi-attribute decision-making is proposed by using the score function to define the attribute importance, and then modifying the closeness function between the evaluation objective and the optimal objective. It is applied to the decision-making of the evaluation scheme of the software reliability growth model. The decision-making results show that the method is feasible and effective, which provides a new solution for determining the attribute weight and making multi-attribute decision-making.