A Self-Management Mechanism to Manage Non-cooperative Behaviors in LGDM-Based Supply Chain Risk Mitigation

Large-scale group decision making (LGDM) is becoming more and more common, and how to assure the security and quality of the decision making process has become a hot topic. Supply chain risk mitigation is a complex LGDM problem involving in many stakeholders. In the decision making process, a group of experts aims at reaching a consensus among alternatives in which non-cooperative behaviors often appear. Some experts might designedly form a small alliance and change their preferences in a direction against consensus with the aim to foster the alliance’s own interests. In this study, we present a novel large-scale consensus reaching framework based on a self- management mechanism to manage non-cooperative behaviors. In the proposed framework, experts are classified into different subgroups using a clustering method, and they provide their evaluation information, i.e., the multi-criteria mutual evaluation matrices (MCMEMs), regarding the obtained subgroups based on their performance. The subgroups’ weights are generated dynamically from the MCMEMs, which are in turn used to update experts’ weights. This mechanism allows penalizing the weights of the experts with non-cooperative behaviors. Detailed comparison analysis is presented to verify the validity of the proposed consensus framework for supply chain risk mitigation.