Optimization of Dynamic Multi-Response Problems Using Grey Multiple Attribute Decision Making

While many of the previous Taguchi method applications dealt with a state system problem, dynamic multi-response problems have received only limited attention. This study presents a practical and systematic procedure to resolve dynamic multi-response problems based on Taguchi's parameter design. The quality loss function is initially applied to assess the quality performance for each response. The technique for order preference by similarity to the ideal solution (TOPSIS), associated with the multiple attribute decision-making (MADM) method, is then incorporated into the Grey relational model of the Grey system theory. The integrated Grey relational grade (IGRG) relative closeness to the ideal solution is determined as a multi-response performance index for determining the optimal parameter setting. The proposed procedure can not only efficiently determine the optimal parameter setting, but also reduce the conflicts when determining the optimal parameter setting for the multi-response problems. Experimental results obtained from the biological reduction of an ethyl acetoacetate process demonstrate the effectiveness of the proposed procedure.