A prioritization approach of non-reciprocal fuzzy preference relations and its extension
A prioritization approach of non-reciprocal fuzzy preference relations and its extension
复制标题
非互易模糊偏好关系的优先排序方法及其扩展
DOI：
10.1016/j.cie.2022.108076
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发表时间：
2022-03
影响因子：
7.9
通讯作者：
Hu Yuan-Kai
中科院分区：
文献类型：
--
作者：
Liu Fang;Yang Hui;Hu Yuan-Kai
在决策问题中，偏好关系是模拟决策者意见的常用工具。从偏好关系中得出优先级是很重要的，以便达到最佳解决方案。在本文中，我们提出了一种基于优化的方法来获得优先向量的模糊偏好关系（FPR）没有加性互逆性（阿普）。首先，非互惠模糊偏好关系（NrFPRs）的概念被用来描述的情况下，没有阿普的FPRs。将区间加性互反矩阵（IARM）分解为两个NrFPR。其次，提出了方差相似性度量来量化NrFPR的两个列/行向量的相似程度。构造了一个双方差一致性指标来衡量NrFPR的不一致程度。提出了一种基于优化的模型，从NrFPRs中提取优先级向量。一些有趣的属性被解决，以显示所提出的方法的有效性。第三，所提出的优先级方法扩展到IARMs的优先级。与现有的IARMs的优先级方法的一些比较报告使用数值例子。观测结果表明，IARM中表现出的不确定性可以被NrFPRs捕获。一些决策模型与IARMs可以重新使用所提出的方法为NrFPRs。
In decision making problems, preference relations are the widely used tools to simulate the opinions of decision makers (DMs). It is important to elicit priorities from a preference relation in order to reach an optimal solution. In this paper, we propose an optimization-based method to derive the priority vector from a fuzzy preference relation (FPR) without additively reciprocal property (ARP). First, the concept of non-reciprocal fuzzy preference relations (NrFPRs) is used to characterize the case of FPRs without ARP. An interval additive reciprocal matrix (IARM) is decomposed into two NrFPRs. Second, the variance similarity measure is proposed to quantify the similarity degree of two column/row vectors of NrFPRs. A double variance consistency index is constructed to measure the inconsistency degree of NrFPRs. An optimization-based model is proposed to elicit the priority vector from NrFPRs. Some interesting properties are addressed to show the effectiveness of the proposed approach. Third, the proposed prioritization method is extended to derive priorities from IARMs. Some comparisons with the existing prioritization methods of IARMs are reported using numerical examples. The observations reveal that the uncertainty exhibiting in IARMs can be captured by NrFPRs. Some decision-making models with IARMs could be revisited using the proposed method for NrFPRs.
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影响因子：
--
作者：
ZADEH, LA
通讯作者：
ZADEH, LA
DOI：
10.1049/pbpo161e_ch3
发表时间：
2021-07
期刊：
Artificial Intelligence for Smarter Power Systems: Fuzzy logic and neural networks
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1016/j.ins.2010.08.019
发表时间：
2010-12
期刊：
Inf. Sci.
影响因子：
--
作者：
S. Genç;F. E. Boran;D. Akay;Zeshui Xu
通讯作者：
S. Genç;F. E. Boran;D. Akay;Zeshui Xu
DOI：
10.1016/j.ins.2012.01.019
发表时间：
2012-06
期刊：
Inf. Sci.
影响因子：
--
作者：
Zhou-Jing Wang;Kevin W. Li
通讯作者：
Zhou-Jing Wang;Kevin W. Li
影响因子：
4.7
作者：
Zeshui Xu
通讯作者：
Zeshui Xu