犹豫模糊集是模糊集理论的最新研究成果，而区间犹豫模糊集是犹豫模糊集的进一步发展和深化，它允许其隶属度可以有多个可能的区间值组成的集合，因而能更加客观地刻画现实中广泛存在的犹豫性和不确定性。对区间犹豫模糊信息集结的研究是犹豫模糊集理论研究中的基础而又十分重要的课题,具有前瞻性和探索性。本项目在利用Archimedean三角模与其余模研究区间犹豫模糊集运算的基础上，系统地研究并提出变量间相互独立、变量间存在相关性、变量间存在优先序以及动态变量的平均型区间犹豫模糊平均型集结算子，分析它们具有的优良性质和特例。在此基础上构造适合属性间独立、属性间存在相关性、属性间存在优先序和多时段四类不同决策情形的群体多属性决策方法。最后，对云服务管理中的云服务质量评价、云服务商选择、云服务绩效分析和云服务安全评估进行应用研究。研究成果将有助于丰富和发展犹豫模糊集理论以及有益于区间犹豫模糊集在决策中的应用和推广。

Hesitant fuzzy set is the latest theoretical result in the field of fuzzy sets theory, while the interval-valued hesitant fuzzy set (IVHFS) is its further extension and generalization, which permits the membership of a element to a set can be expressed with some possible intervals and thus can more exactly and objectively describe the hesitancy and uncertainty existing the real world. The aggregation of IVHFSs is vital and has becoming to a hot research topic. In the project, we first investigate the basic operational laws of IVHFSs based on Archimedean t-norm and t-conorm, then, we systematically and intensively investigate and then develop four families of interval-valued hesitant fuzzy averaging aggregation operators under the situations that the argument variables are independent, correlated, prioritized and dynamic, respectively. Their desirable properties are proofed and special cases are discussed in detailed. Based on the proposed aggregation operators, some group multi-attribute decision making methods with IVHFSs are constructed correspondingly to accommodate the decision situations that the attributes are independent, correlated, prioritized and dynamic. Finally, we utilize the developed methods to deal with the problems of quality evaluation of cloud service, selection of cloud service providers, performance analysis of cloud service, security assessment of cloud services, etc. The investigated results can enrich and extend the hesitant fuzzy theory and facilitate decision makers to make decision under the uncertain and hesitant setting.