Cloud vendors (CVs) play an indispensable role in the development of IT sectors and industry 4.0. Many CVs evolve every day, and a systematic selection of these is becoming substantial for organizations. Literature studies have shown that multicriteria decision-making (MCDM) is a powerful tool for systematic selection. However, the major issue with the state-of-the-art models is that they do not effectively represent uncertainty. Moreover, the personalized selection of CVs based on user queries is not prominent in an MCDM context. In this paper, to circumvent these issues, a new decision framework is proposed that utilizes a generalized preference style called interval-valued probabilistic linguistic term set (IVPLTS). This preference style considers occurring probability values as interval numbers instead of a single precise value, which provides flexibility during preference elicitation. Initially, missing values are imputed systematically by using a case-based method. Then, the consistency of these preferences is checked using Cronbach's alpha coefficient, and the inconsistent preferences are repaired rationally by using an iterative method. A programming model is proposed for determining the weights of the evaluation criteria. Furthermore, Maclaurin symmetric mean (MSM) is extended to IVPLTS for aggregating preferences from each expert. The interval-valued probabilistic linguistic comprehensive (IVPLC) method is proposed for prioritizing CVs in a personalized manner. Finally, the framework's practicality is validated by using a case study of CV selection for an academic institution; strengths and weaknesses of the framework are conferred by comparison with extant CV selection models.

中文翻译：

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使用区间值概率语言信息和未知权重评估云​​供应商

云厂商（CV）在IT行业和工业4.0的发展中扮演着不可或缺的角色。许多简历每天都在演变，对这些简历进行系统的选择对于组织来说变得越来越重要。文献研究表明，多标准决策（MCDM）是系统选择的有力工具。然而，最先进模型的主要问题是它们不能有效地表示不确定性。此外，基于用户查询的个性化简历选择在 MCDM 上下文中并不突出。在本文中，为了规避这些问题，提出了一种新的决策框架，该框架利用称为区间值概率语言术语集（IVPLTS）的广义偏好风格。这种偏好风格将出现的概率值视为区间数而不是单个精确值，这在偏好引发期间提供了灵活性。最初，使用基于案例的方法系统地估算缺失值。然后，利用Cronbach's alpha系数检验这些偏好的一致性，并通过迭代的方法合理修复不一致的偏好。提出了一种规划模型来确定评价标准的权重。此外，Maclaurin 对称均值 (MSM) 被扩展到 IVPLTS，用于聚合每个专家的偏好。提出了区间值概率语言综合 (IVPLC) 方法，用于以个性化方式对 CV 进行优先级排序。最后，通过一个学术机构的简历选择案例研究，验证了该框架的实用性；