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Granulized Z-OWA aggregation operator and its application in fuzzy risk assessment
International Journal of Intelligent Systems ( IF 7 ) Pub Date : 2021-09-24 , DOI: 10.1002/int.22682
Ashish Garg 1 , J. Maiti 1 , Akhilesh Kumar 1
Affiliation  

The concept of granulized Z-numbers improves information utilization and manages the degree of uncertainty in decision-making. In this paper, a novel scoring method, namely, ordered weighted averaging-based expected granulized Z-number, a new aggregation operator, named, granulized Z-number-based ordered weighted averaging operator, and a novel fuzzy risk assessment scheme is proposed. The proposed scoring method is used to order the granulized Z-numbers and takes care of the possibilistic as well as probabilistic information contained in the granulized Z-numbers. The maximum entropy principle-based nonlinear optimization model is formulated to capture the aforesaid probabilistic information during the scoring process. Based on the proposed scoring and ordering method, the granulized Z-number-based ordered weighted averaging operator is developed, which judiciously integrates the benefits of the granulized Z-numbers and the ordered weighted averaging operator to provide an improved aggregation of the decision-making information collected from multiple sources (experts). The required properties are also proved. Finally, the novel fuzzy risk assessment scheme is developed using the granulized Z-number-based ordered weighted averaging operator, the average linkage-based ordered weighted averaged similarity measure between two granulized Z-numbers, and the basic operations of logical gates of a fault tree. This scheme provides the system-level failure probability in an easy-to-understand form with reliability. A case study is also presented to demonstrate the usability and feasibility of the proposed models and schemes.

中文翻译:

粒化Z-OWA聚合算子及其在模糊风险评估中的应用

颗粒化 Z 数的概念提高了信息利用率并管理决策中的不确定性程度。本文提出了一种新的评分方法,即基于有序加权平均的期望粒化Z数,一种新的聚合算子,命名为基于粒化Z数的有序加权平均算子,以及一种新的模糊风险评估方案。建议的评分方法用于对粒化 Z 数进行排序,并处理粒化 Z 数中包含的可能性和概率信息。制定基于最大熵原理的非线性优化模型以在评分过程中捕获上述概率信息。基于所提出的评分和排序方法,开发了基于粒化 Z 数的有序加权平均算子,它明智地整合了细化 Z 数和有序加权平均算子的好处,以提供从多个来源(专家)收集的决策信息的改进聚合。还证明了所需的特性。最后,使用基于粒化 Z 数的有序加权平均算子、两个粒化 Z 数之间基于平均链接的有序加权平均相似性度量以及故障逻辑门的基本操作,开发了新的模糊风险评估方案。树。该方案以易于理解的形式提供具有可靠性的系统级故障概率。还提供了一个案例研究,以证明所提出的模型和方案的可用性和可行性。
更新日期:2021-09-24
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