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Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators

机译:基于直觉模糊交互的多个属性组决策分区Bonferroni均值算子

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摘要

The partitioned Bonferroni mean (PBM) operator and the partitioned geometric Bonferroni mean (PGBM) operator assume that all attributes are partitioned into several parts, where the members in the same part are interrelated, while the members in different parts are not interrelated. They can be used to process multiple attribute group decision making (MAGDM) problems in which attributes are partitioned into serval parts. In this paper, we extend the PBM operator and the PGBM operator based on the interaction operational laws of intuitionistic fuzzy sets (IFSs) to propose the interaction PBM (IFIPBM) operator for intuitionistic fuzzy numbers (IFNs), the weighted interaction PBM (IFWIPBM) operator for IFNs, the interaction PGBM (IFIPGBM) operator for IFNs and the weighted interaction PGBM (IFWIPGBM) operator for IFNs. We also analyze some properties and some special cases of these proposed operators (including the IFIPBM operator, the IFWIPBM operator, the IFIPGBM and the IFWIPGBM operator). Based on the proposed IFWIPBM operator and the proposed IFWIPGBM operator, a novel MAGDM method for IFNs is proposed, and some examples are used to compare the experimental results of the proposed method with the ones of the existing methods. The experimental results show that the proposed method outperforms the existing methods for MAGDM with INFs. (C) 2017 Elsevier Inc. All rights reserved.
机译:分区Bonferroni均值(PBM)操作员和分区几何Bonferroni均值(PGBM)操作员假设所有属性都被划分为几个部分,其中相同部分中的构件是相互关联的,而不同部件中的成员不相互关联。它们可用于处理多个属性组决策(MAGDM)问题,其中属性将属性分为伺服部件。在本文中,我们基于直觉模糊集(IFSS)的交互操作规律来扩展PBM运算符和PGBM运算符,提出用于直觉模糊数(IFNS)的交互PBM(IFIPBM)运算符,加权交互PBM(IFWIPBM)用于IFNS的操作员,用于IFNS的IFNS和IFNS的IFIPGBM(IFIPGBM)运算符IFNS的加权交互PGBM(IFWIPGBM)运算符。我们还分析了一些属性和一些特殊情况的这些建议的运营商(包括IFIPBM运算符,IFWIPBM运算符,IFIPGBM和IFWIPGBM运算符)。基于所提出的IFWIPBM运营商和所提出的IFWIPGBM操作员,提出了一种用于IFNS的MAGDM方法,一些示例用于将所提出的方法的实验结果与现有方法进行比较。实验结果表明,该方法与INFS的现有方法优于现有的MAGDM方法。 (c)2017年Elsevier Inc.保留所有权利。

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