Abstract
With respect to dynamic multi-attribute group decision-making (DMAGDM) problems, where attribute values take the form of intuitionistic fuzzy values (IFVs) and the weights (including expert, attribute and time weights) are unknown, the dynamic intuitionistic fuzzy power geometric weighted average (DIFPGWA) operator and the improved IFVs’ GM(1,1) prediction model (IFVs-GM(1,1)-PM) are proposed. First, the concept of IFVs, the operational rules, the distance between IFVs, and the comparing method of IFVs are defined. Second, the DIFPGWA operator and the improved IFVs-GM(1,1)-PM are defined in detail. Third, corresponding decision-making (D-M) steps are proposed. Three kinds of weights are given by the proposed determination method. Finally, an example is given to prove the effectiveness and superiority of the proposed decision-making method.
Highlights
Decision-making is an activity frequently occurring in management
It is generally believed that the more deviated from the common cognition, the smaller the expert weight should be, and the closer to the common cognition, the greater the expert weight should be
DIF-multi-attribute group decision-making (MAGDM) is an important branch of modern decision theory
Summary
Decision-making is an activity frequently occurring in management. This is the basic idea of modern decision theory. Dynamic intuitionistic fuzzy power geometric weighted average (DIFPGWA) operator that better reflect support relationship between attributes are proposed, which can capture the delicate nuances of the comprehensive evaluation values that the decision makers need to reflect when evaluating. In DIF-MAGDM, the proposed DIFPGWA operator aggregates the information of each stage, and captures the fine nuances of the comprehensive evaluation value that the decision makers want to reflect. This paper forecasts the comprehensive evaluation value of each scheme in the period through the improved IFVs-GM(1,1)-PM This method can make up for the lack of DIFPGWA operator. The multi-period weight determination method proposed in this paper helps to obtain more accurate results. The decision method proposed in this paper is applied to the selection of the company’s intern employees
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