Sakthivel Aicevarya Devi, Augustin Felix
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 600127, Chennai, India.
Sci Rep. 2024 Dec 28;14(1):30961. doi: 10.1038/s41598-024-82046-x.
In the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique is needed to assess uncertainty in real-time complex situations. Therefore, an association between preference and performance with satisfactory score (APPSS) method is introduced as a fuzzy decision-making method that incorporates two components: preference and performance. This method focuses on demonstrating a connection between preference and performance with a satisfactory measure. Preference analysis evaluates the significance of criteria, while performance analysis assesses the effectiveness of each alternative based on these criteria. Additionally, the satisfactory measure ensures the reliability of the outcomes. The applicability of the proposed method is demonstrated by analyzing the impact of COVID-19 on different age groups in India across various categories. The proposed method employs triangular spherical fuzzy numbers (TSFN), which is a mathematical model that extends beyond conventional fuzzy numbers by incorporating both triangular and spherical characteristics. Furthermore, a new scoring function for TSFN is developed using the graded mean integration method. The analysis reveals that the age group between 60-69 is highly vulnerable to COVID-19. The robustness of these outcomes is verified through sensitivity and comparative analyses. The findings also assist policymakers in more effectively assessing potential future health complications.
在当前情况下，决策模型对于分析现实世界的问题至关重要。为了解决这些问题的动态性质，不同的研究人员提出了模糊决策模型。然而，需要一种先进的技术来评估实时复杂情况下的不确定性。因此，引入了一种带满意分数的偏好与绩效关联（APPSS）方法作为一种模糊决策方法，该方法包含两个组成部分：偏好和绩效。此方法着重于用一种满意度量来展示偏好与绩效之间的联系。偏好分析评估准则的重要性，而绩效分析则根据这些准则评估每个备选方案的有效性。此外，满意度量确保了结果的可靠性。通过分析新冠疫情对印度不同年龄组在各个类别上的影响，证明了所提方法的适用性。所提方法采用三角球形模糊数（TSFN），这是一种通过结合三角和球形特征而超越传统模糊数的数学模型。此外，使用分级均值积分法为TSFN开发了一种新的评分函数。分析表明，60 - 69岁年龄组极易感染新冠病毒。通过敏感性分析和比较分析验证了这些结果的稳健性。这些发现还有助于政策制定者更有效地评估未来潜在的健康并发症。
