Multi-attribute group decision-making method with linguistic q-rung orthopair fuzzy information based on bi-direction Choquet integral

Ling Weng, Jian Lin, Shujie Lv
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Abstract

PurposeThe purpose of this paper is to develop the linguistic q-rung orthopair fuzzy set (LqROFS) information VIKOR method based on the bi-direction Choquet integral (BDCI), taking into account the correlation between information. The method can enrich the existing studies related to LqROFS information and better solve the problem of MAGDM problem.Design/methodology/approachSince applying Choquet integral (CI) depict information interaction is a common operation in MAGDM. However, the traditional CI has some limitations. The unidirectional alignment may affect the MAGDM results. Therefore, a LqROFS-VIKOR method based on BDCI is proposed, where BDCI is used to aggregate the decision matrix. Furthermore, it is not reasonable to apply exact numbers to express the similarity between two qualitative data. Then a new method of defining similarity using linguistics is proposed. The similarity is used to calculate attribute weights.FindingsThe validity and potential application of MAGMD method with linguistic q-rung orthopair fuzzy information based on BDCI are demonstrated in a numerical examples study.Originality/valueAccording to the study of available literature, the current research on LqROFS is incomplete. The existing studies of both similarity and aggregate operators have certain shortcomings. The definition of similarity proposed in this paper is more in line with reality. And compared with the existing methods, the BDCI-based aggregate operator can describe the interaction between information more reasonably. Based on this VIKOR method based on BDCI under the LqROFS environment can better select the alternative.
基于双向Choquet积分的语言q阶正交模糊信息多属性群决策方法
目的考虑信息之间的相关性,建立基于双向Choquet积分(BDCI)的语言q阶正交模糊集(LqROFS)信息VIKOR方法。该方法可以丰富现有的LqROFS相关研究信息,更好地解决MAGDM问题。设计/方法/方法由于利用Choquet积分(CI)描述信息交互是MAGDM中常用的操作。然而,传统的CI有一些局限性。单向对准可能会影响MAGDM结果。为此,提出了一种基于BDCI的LqROFS-VIKOR方法,该方法利用BDCI对决策矩阵进行聚合。此外,用精确的数字来表示两个定性数据之间的相似性是不合理的。在此基础上,提出了一种新的用语言学定义相似性的方法。利用相似度计算属性权重。结果通过数值算例研究,验证了基于BDCI的基于语言q阶正交模糊信息的MAGMD方法的有效性和应用潜力。根据现有文献的研究,目前对LqROFS的研究是不完整的。现有的相似算子和聚集算子的研究都存在一定的不足。本文提出的相似性定义更符合实际。与现有方法相比,基于bdci的聚合算子能更合理地描述信息之间的交互关系。基于这种基于BDCI的VIKOR方法在LqROFS环境下可以更好地选择备选方案。
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