Multi-attribute Group Decision Making Based on Granular Computing

By simulating the process of human thinking,a solving method for multi-attribute group decision making problem based on granular computing was proposed.Firstly,mathematical description of granular computing structure model was discussed.Secondly,in order to give a fine description to single decision maker's thought at different granular layers,granular information entropy measurement under relative meaning was given.Through definitions of matching rate and coverage rate under granular divisions,the optimization of granular layer based on similar threshold was carried out,and the final optimization result,which indicates single decision maker's decision thought,was exhibited by weight values of feature attributes.Thirdly,based on values of weight vectors which indicate decision makers' decision thought upon different granular layers,the nonlinear optimization model was built up to rebalance all decision makers' opinions at different granular layers and get the final optimal weight values of feature attributes,which can be recognized by all decision makers.Thus,an integrated and qualitative problem solving environment for multi-attribute group decision making was reached.Lastly,a graduate student admission interview assessment example was given to prove its feasibility and superiority.