Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices

Group decision making plays an important role in various fields of management decision and economics. In this paper, we develop two methods for hesitant fuzzy multiple criteria group decision making with group consensus in which all the experts use hesitant fuzzy decision matrices （HFDMs） to express their preferences. The aim of this paper is to present two novel consensus models applied in different group decision making situations, which are composed of consensus checking processes, consensus-reaching processes, and selection processes. All the experts make their own judgments on each alternative over multiple criteria by hesitant fuzzy sets, and then the aggregation of each hesitant fuzzy set under each criterion is calculated by the aggregation operators. Furthermore, we can calculate the distance between any two aggregations of hesitant fuzzy sets, based on which the deviation between any two experts is yielded. After introducing the consensus measure, we develop two kinds of consensus-reaching procedures and then propose two step-by-step algorithms for hesitant fuzzy multiple criteria group decision making. A numerical example concerning the selection of selling ways about ＇Trade-Ins＇ for Apple Inc. is provided to illustrate and verify the developed approaches. In this example, the methods which aim to reach a high consensus of all the experts before the selection process can avoid some experts＇ preference values being too high or too low. After modifying the previous preference information by using our consensus measures, the result of the selection process is much more reasonable.

群决策在管理决策和经济领域有着重要作用。本文建立了两种关于犹豫模糊多准则群共识决策的方法,并使用犹豫模糊决策矩阵(hesitant fuzzy decision matrices,HFDMs)表示专家的偏好。本文建立了两个新颖的、可应用于不同群决策环境的共识模型,其中共识决策过程包括:共识检验过程,共识达成过程和选择过程。专家以犹豫模糊集的方式表达自己的选择,然后通过集成算子计算每个准则下的犹豫模糊集。此外,基于两个专家在决策之间的偏差计算出任意两个犹豫模糊集之间的距离。本文建立了两种达成共识的方法,并提出了两种犹豫模糊多准则群决策方法的具体计算步骤。最后通过具体案例分析了苹果公司以旧换新的决策过程。该案例旨在让所有专家在进行选择决策之前达成高度共识,避免一些专家的偏好值过高或过低。通过共识测量修正的偏好信息可令决策结果更加合理。