Dynamic grey incidence group decision making methodology based on interval two-tuple linguistic information processing and application

With respect to the problem that index value is ofter given in the form of interval linguistic assessment information in the dynamic multiple attribute group decision making, a novel dynamic grey incidence group decision making methodology based on interval two-tuple linguistic information is proposed. Firstly, the algorithms and properties of the interval two-tuple linguistic information are used to aggregate the information evaluation matrix. Secondly, the positive and negative ideal schemes for each time period are designed in order to deal with the problem that the expert weights and time weights are known, while index weights are unknown, so that the multi-objective programming model with the minimum deviation of the grey incidence degree between each scheme and the positive ideal scheme for each stage is built to determine the index weights. Thirdly, the grey incidence each stage degree with interval two-tuple linguistic information between each scheme and the positive and negative ideal scheme for each stage is calculated to establish the optimization model of the optimal membership degree for scheme, so that the expressions form of the optimal membership degree for scheme is determined to solve the optimal membership degree for scheme. Finally, an example validates the feasibility and effectiveness of the novel model. The results show that the proposed method can more precisely deal with the language information and avoid information distortion and loss based on the previous method of processing the language.