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Multiple Attributes Group Decision-Making Approaches Based on Interval-Valued Dual Hesitant Fuzzy Unbalanced Linguistic Set and Their Applications

机译:基于区间值双重犹豫模糊不平衡语言集的多属性群决策方法及其应用

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Aiming at multiple attributes group decision-making (MAGDM) problems that characterize uncertainty nature and decision hesitancy, firstly, we propose the interval-valued dual hesitant fuzzy unbalanced linguistic set (IVDHFUBLS) in which two sets of interval-valued hesitant fuzzy membership degrees and nonmembership degrees are employed to supplement the most preferred unbalanced linguistic term, as an effective hybrid expression tool to elicit complicate preferences of decision-makers more comprehensively and flexibly than existing tools based on classic linguistic term set. Basic operations for IVDHFUBLS are further defined; also a novel distance measure is developed to avoid potential information distortion that could be brought about by traditional complementing methodology for hesitant fuzzy set and its derivatives. In view of the fundamental role of aggregation operators in MAGDM modelling, we next develop some extended power aggregation operators for IVDHFUBLS, including power aggregation operator, weighted power aggregation operator, and induced power ordered weighted aggregation operator; their desirable properties and special cases are also analyzed theoretically. Subsequently, with support of the above methods, we develop two effective approaches for our targeted complex decision-making problems and verify their effectiveness and practicality by numerical studies.
机译:针对具有不确定性和决策犹豫性的多属性群决策问题,首先提出了区间值双重犹豫模糊不平衡语言集(IVDHFUBLS),其中两组区间值犹豫模糊隶属度和与基于经典语言术语集的现有工具相比,非成员学位被用来补充最优选的不平衡语言术语,作为一种有效的混合表达工具,可以使决策者的偏好更加全面和灵活。进一步定义了IVDHFUBLS的基本操作。还开发了一种新颖的距离度量,以避免潜在的信息失真,而传统的互补方法可能会对犹豫的模糊集及其派生词带来失真。考虑到聚合算子在MAGDM建模中的基本作用,我们接下来为IVDHFUBLS开发一些扩展的功率聚合算子,包括功率聚合算子,加权功率聚合算子和诱导功率有序加权聚合算子。从理论上分析了它们的理想特性和特殊情况。随后,在上述方法的支持下,我们针对目标复杂的决策问题开发了两种有效方法,并通过数值研究验证了其有效性和实用性。

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