Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

基于单值中智决策算法的金融产品评价模型

借助于偏好关系和单值中智集的优点,首先引入单值中智偏好关系来处理评价过程中的不精确和不一致信息,并运用模糊判断矩阵的乘性一致性,给出了单值中智偏好关系的乘性一致性概念;其次,探究了一种单值中智偏好关系的设计方法,使得其拥有完全乘性一致性,并建立了一种新型一致性局部调整算法,同时证明算法的收敛性;最后,结合余弦相似度,设计了基于单值中智偏好关系一致性调整算法的金融产品评价模型,并通过金融产品选择实例说明该模型的可靠性。