Group decision-making (GDM) implies a process of extracting wisdom from a group of experts. In this study, a novel GDM model is proposed by applying the particle swarm optimization (PSO) algorithm to simulate the consensus process within a group of experts. It is assumed that the initial positions of decision-makers (DMs) are characterized by pairwise comparison matrices (PCMs). The minimum and maximum of the entries in the same locations of individual PCMs are supposed to be the constraints of DMs’ opinions. The novelty comes with the construction of the optimization problem by considering the group consensus and the consistency degree of the collective PCM. The former is to minimize the distance between the collective PCM and each individual one. The latter is to make the collective PCM be acceptably consistent in virtue of the geometric consistency index. The fitness function used in the PSO algorithm is the linear combination of the two objectives. The proposed model is applied to solve a large-scale GDM problem arising in emergency management. Some comparisons with the existing methods reveal that the developed model has the advantages to decrease the order of an optimization problem and reach a fast yet effective solution.

小组决策（GDM）意味着从专家小组中提取智慧的过程。在这项研究中，通过应用粒子群优化（PSO）算法在一组专家中模拟共识过程，提出了一种新的GDM模型。假定决策者（DM）的初始位置由成对比较矩阵（PCM）来表征。单个PCM的相同位置中条目的最小和最大值被认为是DM意见的约束。通过考虑群体共识和集体PCM的一致性程度来构造优化问题是一种新颖。前者是为了最小化集体PCM与每个个人PCM之间的距离。后者是根据几何一致性指标使集体PCM具有可接受的一致性。PSO算法中使用的适应度函数是两个目标的线性组合。该模型用于解决应急管理中出现的大规模GDM问题。与现有方法的一些比较表明，所开发的模型具有减少优化问题的顺序并获得快速而有效的解决方案的优点。