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A Group Decision Making Method with Interval-Valued Intuitionistic Fuzzy Preference Relations and Its Application in the Selection of Cloud Computing Vendors for SMEs

机译:具有间隔valuitionisticfizzy偏好关系的组决策方法及其在选择中小企业云计算供应商中的应用

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摘要

To solve the problem of choosing the appropriate cloud computing vendors in small and medium-sized enterprises (SMEs), this paper boils it down to a group decision making (GDM) problem. To facilitate the judgment, this paper uses preference relation as the decision making technology. Considering the situation where uncertain positive and negative judgments exist simultaneously, interval-valued intuitionistic fuzzy preference relations (IVIFPRs) are employed to express the decision makers' judgments. In view of the multiplicative consistency and consensus analysis, a new GDM algorithm with IVIFPRs is offered. To accomplish this goal, a new multiplicative consistency is first defined, which can avoid the limitations of the previous ones. Then, a programming model is built to check the consistency of IVIFPRs. To deal with incomplete IVIFPRs. two programming models are constructed to determine the missing values with the goal of maximizing the level of multiplicative consistency and minimizing the total uncertainty. To achieve the minimum adjustment of original preference information, a programming model is established to repair inconsistent IVIFPRs. In addition, programming models for getting the decision makers (DMs)' weights and improving the consensus degree are offered. Finally, a practical decision making example is given to illustrate the effectiveness of the proposed method and to compare it with previous methods.
机译:为解决在中小企业中选择合适的云计算供应商(中小企业)的问题,本文将其归结为群体决策(GDM)问题。为了促进判断,本文使用偏好关系作为决策技术。考虑到同时存在不确定的积极和负面判断的情况,就会采用间歇性直观模糊偏好关系(IVIFPRS)来表达决策者的判决。鉴于乘法一致性和共识分析,提供了一种具有IVIFPRS的新GDM算法。为了实现这一目标,首先定义了一种新的乘法一致性,可以避免前一个的局限性。然后,构建编程模型以检查IVIFPRS的一致性。处理不完整的IVIFPRS。构建两个编程模型以确定缺失的值,目标最大化乘法一致性水平,最大限度地减少总不确定性。为了实现原始偏好信息的最小调整,建立了一种编程模型来修复不一致的IVIFPR。此外,还提供了用于获取决策者(DMS)权重以及提高共识学位的编程模型。最后,给出了实际决策示例来说明所提出的方法的有效性并与先前的方法进行比较。

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