Defective alternatives detection-based multi-attribute intuitionistic fuzzy large-scale decision making model.

This paper focuses on multi-attribute intuitionistic fuzzy large-scale decision making (LSDM) scenarios. The alternatives are described by attributes in the LSDM model. The decision failure may be caused by unqualified alternative being the final decision. To avoid this, we propose a Defective Alternative Detection-based multi-attribute intuitionistic fuzzy LSDM (DAD-LSDM) model. The model consists of two stages: the Defective Alternatives Detection (DAD) stage and the corresponding Intuitionistic Fuzzy Consensus Reaching Process (IF-CRP) stage. In the DAD stage, it is easy to recognize the defective alternatives by calculating the attributes’ scores and to accordingly improve them with the attributes against the corresponding alternatives. In the IF-CRP stage, by utilizing an intuitionistic fuzzy clustering method and similarity calculation, we detect and manage the potential non-cooperative decision makers to increase the consensus degree of the LSDM event. By implementing the DAD stage before the IF-CRP stage, we can avoid those excessively defective alternatives to be chosen and can also improve the quality of slightly defective alternatives. It not only decreases the risk of decision failure and improves the feasibility of the provided alternatives, but also guarantees the validity and scientificity of the following IF-CRP stage. With a numerical example, we show the DAD-LSDM model can well detect and classify the defective alternatives as well as improve the slightly defective alternatives. The decision makers finally reach a high consensus with detecting and managing the non-cooperative decision makers. The DAD-LSDM model is feasible and efficient in practice for the intuitionistic fuzzy LSDM scenarios.