Naz Sumera, Fatima Areej, But Shariq Aziz, Pamucar Dragan, Zamora-Musa Ronald, Acosta-Coll Melisa
Department of Mathematics, Division of Science and Technology, University of Education, Lahore, Pakistan.
School of Systems and Technology, Department of Computer Science, University of Management and Technology, Lahore, Pakistan.
Heliyon. 2024 Jun 18;10(12):e33004. doi: 10.1016/j.heliyon.2024.e33004. eCollection 2024 Jun 30.
This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic -rung orthopair fuzzy set (PL-ROFS) to handle the inherent uncertainties associated with astronomical data. The PL-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PL-ROF weighted power average (PL-ROFWPA) and the PL-ROF weighted power geometric (PL-ROFWPG). These operators form the foundation of a novel method within the PL-ROF environment. Furthermore, this study integrates the PL-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PL-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PL-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.
本研究采用一种基于模糊逻辑的新型框架来解决现代天文学中常见的多属性群体决策问题。我们的方法利用概率语言梯形正交对模糊集（PL-ROFS）来处理与天文数据相关的固有不确定性。与现有的模糊集（如概率犹豫模糊集、语言直觉模糊集和语言毕达哥拉斯模糊集）相比，PL-ROFS具有显著优势，这些现有模糊集同时包含随机和非随机不确定性。为了有效聚合概率语言决策信息，我们提出了两个新型算子：PL-ROF加权幂平均（PL-ROFWPA）和PL-ROF加权幂几何（PL-ROFWPG）。这些算子构成了PL-ROF环境下一种新方法的基础。此外，本研究将PL-ROF框架与VIseKriterijumska Optimizacija I Kompromisno Resenje（VIKOR）模型集成，VIKOR模型是一种广泛使用的决策工具，以其能够在群体效用最大化与个体遗憾最小化之间取得平衡而闻名。这种集成产生了PL-ROF-VIKOR模型，这是一种基于决策者主观偏好对备选解决方案进行排序的新方法。通过天文学中的一个实际案例研究，并辅以参数分析和比较分析，证明了所提方法的有效性。这些分析突出了PL-ROF-VIKOR模型的效率和准确性，最终得出结论：在本案例研究中，宇宙学是最优化的关键发现。
