在COVID-19大流行期间使用TOPSIS技术进行公平的床位分配,该技术基于区间值的皮塔哥拉斯模糊超软集的相关系数
Rana Muhammad Zulqarnain1, Wen-Xiu Ma2, Imran Siddique3
1School of Mathematical Sciences, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.
Scientific reports
|April 1, 2024
概括
本研究介绍了区间值的毕达哥拉斯模糊超软集 (IVPFHSS) 的相关系数,增强了复杂场景中的统计分析. 这些新措施改善了决策,正如COVID-19大流行期间优化医院病床分配所证明的那样.
科学领域:
- 统计和决策科学 统计和决策科学
- 模糊的集合理论 模糊的集合理论
- 数据分析 数据分析
背景情况:
- 统计分析的准确性取决于数据质量,数据质量可能不清楚或难以解释.
- 传统的相关系数通常不适用于区间值的毕达哥拉斯模糊超软集 (IVPFHSS).
- IVPFHSS为更精确和准确的数据分析提供了一个通用的框架.
研究的目的:
- 为IVPFHSS引入相关系数 (CC) 和加权相关系数 (WCC).
- 探索这些新定义的相关性指标的基本特性.
- 展示CC和WCC在决策中的实际应用,特别是使用TOPSIS模型在COVID-19大流行期间分配医院病床.
主要方法:
- 为IVPFHSS量身定制的相关系数 (CC) 和加权相关系数 (WCC) 的开发.
- 应用一种技术,以类似于理想解决方案 (TOPSIS) 模型的顺序偏好来确定优先级.
- 数字调查,包括敏感性分析,以评估决策结构.
主要成果:
- 该研究成功地定义和探索了IVPFHSS的CC和WCC的特性.
- 拟议的方法用于优化COVID-19大流行期间的医院床位分配,证明了其有效性.
- 与普遍的模型相比,开发的算法在确定最佳配置方面显示出更一致的效率.
结论:
- 在IVPFHSS中整合相关性措施为不确定的环境中的决策提供了宝贵的见解.
- 开发的多属性决策 (MADM) 方法论对于复杂的问题是强大而有意义的.
- 未来的工作包括开发基于生物地理学的动态床位分配算法,用于增强决策系统.
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