The dynamic and changing nature of the real decision-making environment causes shifts in decision-makers'preferences,thereby altering the decision information available to them at different time points and ultimately impacting the decision results.Therefore,addressing the multi-attribute group decision making(MAGDM)problem with consideration of changes in decision information is essential.A multi-attribute group decision making method was proposed,which considered changes in decision information to obtain evaluation results that aligned more closely with reality.Firstly,based on the principle of maximizing expert consensus,a nonlinear programming model was constructed to obtain expert weights according to the evaluation information of decision unit attributes by experts.The expert weights were adjusted based on changes in the evaluation information of decision unit attributes by experts.Secondly,using the structural entropy weight method,attribute weights were determined based on the sorting information of attributes by experts,and then adjusted according to changes in the sorting of attribute importance by experts.Finally,the EDAS method was used to rank the decision units.An example of supplier evaluation was presented to verify the reliability and effectiveness of the proposed method.
MAGDMexpert weightattribute weightdynamic decision information
NETL
NSTL
万方数据
