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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >A consensus reaching process dealing with comparative linguistic expressions for group decision making: A fuzzy approach
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A consensus reaching process dealing with comparative linguistic expressions for group decision making: A fuzzy approach

机译:达成过程达成群体决策比较语言表达的过程:模糊方法

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摘要

Group Decision Making (GDM) deals with decision problems in which multiple experts, with their own attitudes and knowledge, evaluate different alternatives or solutions with the aim of achieving a common solution. In such cases disagreements can appear, which might led to failed solutions. To manage such conflicts, Consensus Reaching Processes (CRPs) have been added to the GDM solving process. GDM problems under uncertainty often model uncertainty by linguistic descriptors, being most of linguistic based CRPs based on the use of single linguistic terms for modelling experts' opinions, which cannot be expressive enough in some situations because of either the uncertainty involved or the experts' hesitancy. Therefore, this paper aims to fill this gap by proposing a novel consensus model dealing with GDM problems in which experts' preferences are elicited by means of Comparative Linguistic Expressions (CLEs) based on Hesitant Fuzzy Linguistic Term Sets, which allow to model the experts' hesitancy in a flexible way. Furthermore, CLEs are modelled by fuzzy membership functions in order to keep the fuzzy representation in the whole CRP and preserve as much information as possible. Additionally, the proposed model is implemented and integrated in an intelligent CRP support system, so-called AFRYCA 3.0 to carry out a case study about this new CRP and compare it with previous models.
机译:集团决策(GDM)涉及多个专家的决策问题,具有自己的态度和知识,评估不同的替代品或解决方案,以实现共同的解决方案。在这种情况下,可能会出现分歧,这可能导致解决方案失败。为了管理此类冲突,已添加到GDM解决过程中达成进程(CRP)的共识。 GDM在不确定性下的问题经常通过语言描述符模拟不确定性,这是基于语言的大多数基于语言的CRP,基于使用单一语言来建模专家意见,这在某些情况下不能表达,因为所涉及的不确定性或专家犹豫不决。因此,本文旨在通过提出涉及基于比较语言表达(CLES)的GDM问题的新的共识模型来填补这种差距,其中基于比较语言表达式(CLES)允许模糊语言术语集,允许建模专家们'以灵活的方式犹豫不决。此外,CLES由模糊隶属函数建模,以便在整个CRP中保持模糊表示并保持尽可能多的信息。此外,所提出的模型是在智能CRP支持系统中实现和集成的,所谓的AFRyca 3.0,以便对此新CRP进行案例研究,并将其与以前的模型进行比较。

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