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Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

机译:权重信息不完整的二元语言多属性群决策的灰色关联分析方法

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摘要

With respect to 2-tuple linguistic multiple attribute group decision making problems with incomplete weight information, some basic concepts and operational laws of 2-tuple linguistic variables are introduced. An optimization model based on the maximizing deviation method, by which the attribute weights can be determined, is established. According to the traditional ideas of grey relational analysis (GRA), the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and 2-tuple linguistic positive ideal solution and 2-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easv to calculate.
机译:针对权重信息不完整的二元语言多属性群决策问题,介绍了二元语言变量的一些基本概念和操作规律。建立了基于最大偏差法的优化模型,通过该模型可以确定属性权重。根据传统的灰色关联分析(GRA)的思想,通过计算每个备选方案的灰色关联的语言程度以及2元组语言正理想解和2元语言负理想解来确定最佳替代项。基于这样的概念,最优替代方案应具有与正理想解相比最大的灰度关系,而与负理想解相比具有最小的灰度关系。该方法在语言信息处理中具有确切的特点。它避免了以前在语言信息处理中出现的信息失真和丢失。最后,通过一个数值例子来说明所提出方法的使用。结果表明该方法简单,有效,易于计算。

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