A Customized Metaheuristic Approaches for Improving Supplier Selection in Intelligent Decision Making

This paper proposes a metaheuristic approach for Group Decision Making (GDM) model for integrating heterogeneous information. Instead of converting heterogeneous information into a single form, the proposed approach incorporates heterogeneous information using a Weighted Power Average (WPA) operator to prevent information loss. The consensus degree between the individual and the group (decision matrix) is then determined on the basis of the deviation degree. In addition, to adjust the individual decision matrix, the iterative algorithm's feedback mechanism is used, which does not achieve consensus. The consensus GDM is used by the Analytic Hierarchy Process (AHP), an imperative technique for generating weights for each and every criteria. These weights are optimized by using Jaya, one of the metaheuristic algorithms. In addition, in order to choose the best alternative, a heterogeneous Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used. The supplier selection problem is chosen to validate the proposed model and compare it with other similar GDM models. The results show that the proposed approach not only prevents the loss of information, but can also effectively integrate heterogeneous information in the heterogeneous GDM environment.