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Multi-attribute Group Decision Making Method with Unknown Attribute Weights Based on the Q-rung Orthopair Uncertain Linguistic Power Muirhead Mean Operators
International Journal of Computers Communications & Control ( IF 2.7 ) Pub Date : 2021-04-16 , DOI: 10.15837/ijccc.2021.3.4214
Hongmei Zhao , Runtong Zhang , Ao Zhang , Xiaomin Zhu

Q-rung orthopair uncertain linguistic sets (q-ROULSs) are a powerful tool for describing ambiguity and uncertainty of linguistic information. In this study, considering that in most multi-attribute group decision making (MAGDM) problems, not only the quantitative evaluation information of decision makers but also the qualitative evaluation opinions should be considered. Therefore, we develop a novel MAGDM method with unknown attribute weights under the q-rung orthopair uncertain linguistic environments. We firstly propose the cross-entropy of q-ROULSs, which is utilized to solve the optimal attribute weights by a linear programming model. In order to effectively summarize the unclear language information of q-ROULSs, we extend the power Muirhead mean (PMM) operator to q-ROULSs, and propose a family of q-rung othpair uncertain linguistic power Muirhead mean (q-ROULPMM) operators. The advantage of the PMM operator is that it not only mitigates the adverse effects of too high or too low attribute values on the results, but also takes into account the interrelationships between attribute values. At the same time, some ideal properties and special cases of the q-ROULPMM operator are also studied. Further, a new method based on the proposed cross-entropy and aggregation operators is developed for solving the MAGDM problem under q-ROULSs. Finally, we carried out numerical experiments to prove the effectiveness and superiority of the method

中文翻译:

基于Q-rung Orthopair不确定语言能力Muirhead均值算子的未知属性权重多属性群决策方法

Q-rung orthopair 不确定语言集 (q-ROULSs) 是描述语言信息的模糊性和不确定性的强大工具。在本研究中,考虑到在大多数多属性群决策(MAGDM)问题中,不仅要考虑决策者的定量评价信息,还应考虑定性评价意见。因此,我们开发了一种在 q-rung orthopair 不确定语言环境下具有未知属性权重的新 MAGDM 方法。我们首先提出了 q-ROULS 的交叉熵,它被用来通过线性规划模型求解最优属性权重。为了有效总结 q-ROULSs 不清楚的语言信息,我们将幂 Muirhead 均值 (PMM) 算子扩展到 q-ROULSs,并提出了一系列 q-rung othpair 不确定语言能力 Muirhead 均值 (q-ROULPMM) 算子。PMM算子的优点是不仅减轻了过高或过低属性值对结果的不利影响,而且还考虑了属性值之间的相互关系。同时,还研究了q-ROULPMM算子的一些理想性质和特例。此外,开发了一种基于所提出的交叉熵和聚合算子的新方法,用于解决 q-ROULS 下的 MAGDM 问题。最后,我们进行了数值实验,证明了该方法的有效性和优越性。还要考虑属性值之间的相互关系。同时,还研究了q-ROULPMM算子的一些理想性质和特例。此外,开发了一种基于所提出的交叉熵和聚合算子的新方法,用于解决 q-ROULS 下的 MAGDM 问题。最后,我们进行了数值实验,证明了该方法的有效性和优越性。还要考虑属性值之间的相互关系。同时,还研究了q-ROULPMM算子的一些理想性质和特例。此外,开发了一种基于所提出的交叉熵和聚合算子的新方法,用于解决 q-ROULS 下的 MAGDM 问题。最后,我们进行了数值实验,证明了该方法的有效性和优越性。
更新日期:2021-06-22
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