This study proposes a non-threshold consensus model that combines the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making (MALGDM). First, the large-scale experts is classified into several clusters via the combination of the similarities of evaluation information, unit consensus cost, and adjustment willingness. Then, a more sensitive consensus measure method that combines the mean value and variance of the similarities among clusters is presented. Next, a comprehensive identification rule is put forward to determine the cluster with a low consensus level, low unit consensus cost, and high adjustment willingness for information adjustment. An optimization model that combines the minimization of the cost of the cluster and the maximization of the increase of the global consensus level is then constructed to obtain the adjusted information. Also, the adjustment willingness is considered in the constraints to limit the adjustment range. Moreover, instead of the use of a predefined threshold and a maximum number of iterations, a termination index is developed to terminate the consensus reaching process (CRP) to make the CRP more objective and rational. Finally, an application example is presented, and comparison and simulation analyses are performed to validate the feasibility and effectiveness of the proposed model.

本研究提出了一种将最小成本和最大共识增加相结合的非阈值共识模型，用于多属性大群体决策（MALGDM）。首先，结合评价信息的相似性、单位共识成本和调整意愿，将大规模专家划分为多个集群。然后，提出了一种更敏感的共识度量方法，该方法结合了聚类之间相似性的平均值和方差。接下来，提出综合识别规则，确定共识水平低、单位共识成本低、信息调整意愿高的集群。然后构建将集群成本最小化和全局共识级别增加最大化相结合的优化模型，以获得调整后的信息。此外，在约束中考虑调整意愿以限制调整范围。此外，不是使用预先定义的阈值和最大迭代次数，而是制定终止指数来终止共识达成过程（CRP），使CRP更加客观和合理。最后给出了一个应用实例，并进行了对比和仿真分析，验证了所提出模型的可行性和有效性。不是使用预先定义的阈值和最大迭代次数，而是制定终止指标来终止共识达成过程（CRP），使CRP更加客观和合理。最后给出了一个应用实例，并进行了对比和仿真分析，验证了所提出模型的可行性和有效性。不是使用预先定义的阈值和最大迭代次数，而是制定终止指标来终止共识达成过程（CRP），使CRP更加客观和合理。最后给出了一个应用实例，并进行了对比和仿真分析，验证了所提出模型的可行性和有效性。