In order to deal with a complex decision making problem, a group of experts are commonly invited to express their opinions and reach a final decision. For the purpose of building consensus among the members of the group, it is requisite to include iterative mechanisms of brain storming. The particle swarm optimization (PSO) method can be used to model the interactive process of forming decisions. In this paper, we propose a modified consensus model of group decision making augmented by an allocation of information granularity. Under a level of information granularity, it is found that the consistency indexes of randomly created multiplicative reciprocal matrices in the analytic hierarchy process may be bigger than unity. To alleviate this limitation, a modified objective function is proposed, and it is optimized by using the modified PSO method. The information granularity is allocated by considering the reciprocity of preference relations. Some comparative studies are carried out to illustrate the proposed consensus model through numerical examples. The observations reveal that a more consistent decision can be achieved by the proposed approach.

中文翻译：

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一种改进的具有信息粒度分配的群体决策共识模型

为了处理复杂的决策问题，通常会邀请一组专家发表意见并做出最终决定。为了在小组成员之间建立共识，必须包含头脑风暴的迭代机制。粒子群优化 (PSO) 方法可用于对形成决策的交互过程进行建模。在本文中，我们提出了一种改进的群体决策共识模型，该模型通过信息粒度的分配来增强。在一个信息粒度级别下，发现在层次分析过程中随机创建的乘法互易矩阵的一致性指标可能大于1。为了缓解这种限制，提出了一种改进的目标函数，并使用改进的 PSO 方法对其进行了优化。通过考虑偏好关系的互易性来分配信息粒度。进行了一些比较研究，通过数值例子来说明所提出的共识模型。观察结果表明，所提出的方法可以实现更一致的决策。