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A group decision-making method considering both the group consensus and multiplicative consistency of interval-valued intuitionistic fuzzy preference relations

机译:考虑到群体的共识和间隔直观模糊偏好关系的组共识和乘法一致性的组决策方法

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This paper investigates a group decision-making (GDM) method that considers group consensus and multiplicative consistency of interval-valued intuitionistic fuzzy (IVIF) preference relations (IVIFPRs). First, the mean and variance of IVIF values (IVIFVs) are defined and a ranking method for IVIFVs is proposed considering the risk attitude of the expert. Then, the group consensus is presented by the individual similarity between experts. An iteration algorithm is designed to improve the group consensus. A statistical comparative analysis validates this algorithm. Subsequently, a new multiplicative consistency of IVIFPR is defined based on the multiplicative consistency of interval fuzzy preference relation. Two single-objective programming models are established to extract the most optimistic and pessimistic interval priority weight vectors from an IVIFPR, respectively. In particular, if the feasible domains of these two models are empty, two adjusted programs are constructed to replace the originals. Combining the most optimistic and pessimistic interval priority weights, the IVIF priority weights are generated. Further, expert weights are derived from Markov model and used to derive the collective IVIFPR for generating the IVIF priority weights. Therefore, a new method is proposed to solve the GDM with IVIFPRs. Finally, two cases are analyzed to verify the effectiveness of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.
机译:本文调查了一组决策(GDM)方法,其考虑了间隔互向直觉模糊(IVIF)偏好关系(IVIFPRS)的群体共识和乘法一致性。首先,定义IVIF值(IVIFV)的平均值和方差,提出了考虑专家的风险态度的IVIFV的排名方法。然后,专家之间的个人相似性呈现了本集团的共识。迭代算法旨在改善组共识。统计比较分析验证了该算法。随后,基于间隔模糊偏好关系的乘法一致性来定义IVIFPR的新乘法一致性。建立两个单目标编程模型,以分别从IVIFPR中提取最乐观和悲观的间隔优先级权重向量。特别是,如果这两个模型的可行域是空的,则构造两个调整​​后的程序以更换原件。结合最乐观和悲观的间隔优先级权重,生成IVIF优先级权重。此外,专家权重来自Markov模型,并且用于导出用于产生IVIF优先权权重的集体IVIFPR。因此,建议使用IVIFPRS解决GDM的新方法。最后,分析了两种情况以验证所提出的方法的有效性。 (c)2018年Elsevier Inc.保留所有权利。

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