Dynamic subgroup-quality-based consensus in managing consistency, nearness, and evenness quality indices for large-scale group decision making under hesitant environment

With the increasing complexity of decision-making environment and the development of societal demands, large-scale group decision making (LSGDM) has become a hot topic in recent years. Due to the demand of the current decision-making environment, up to now, studies regarding LSGDM methods focussing on different approaches have been published. However, there are few studies that focus on evaluating the results of LSGDM problems. This study proposes three subgroup quality indices, namely, consistency degree, nearness degree and evenness degree, to measure the effectiveness of LSGDM methods. These three quality indices not only can evaluate the effect of a subgroup on the global group, but also can measure the effect of final decision results. Furthermore, the consistency measure is used to design the weights of subgroups, based on which an adaptive dynamic consensus reaching process is introduced. An illustrative example is given to verify the applicability and effectiveness of our proposed model.