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Z数字网络数据包裹分析方法:关于伊朗保险业的案例研究
Fatemeh Sadat Seyed Esmaeili1, Emran Mohammadi1
1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
PloS one
|July 11, 2024
概括
本研究介绍了模糊网络数据包裹分析 (FNDEA),用于在数据不确定性下评估两阶段系统. 新的模糊网络数据包裹分析 (FNDEA) 方法有效地测量和排名模糊环境中的实体.
科学领域:
- 运营研究 运营研究
- 管理科学 管理科学
- 决策分析 决策分析
背景情况:
- 评估具有不确定的数据的复杂系统的性能是具有挑战性的.
- 传统的数据环境分析 (DEA) 方法与模糊性和网络结构作斗争.
研究的目的:
- 提出一种创新的模糊网络数据包围分析 (FNDEA) 方法.
- 在数据不确定性下评估双阶段网络决策单元 (DMU) 的性能.
- 开发一个强大的模型,能够处理Z信息和模两可.
主要方法:
- 非合作游戏 (领导者-追随者) NDEA,Z数概念,可信性理论和机会受限编程 (CCP) 的整合.
- 开发一个线性FNDEA模型,适合在模两可的情况下进行排名.
- 对于不确定的环境而言,独特的效率分解方法.
主要成果:
- 拟议的FNDEA方法有效地处理Z信息和数据模糊性.
- 通过对伊朗私人保险公司的实施,证明了适用性和有效性.
- Z数字网络数据包裹分析 (ZNDEA) 方法被证明适用于测量和排名具有模两可的数据的保险公司.
结论:
- FNDEA方法提供了一种线性和有效的方法来评估具有不确定的数据的双阶段DMU.
- 该方法在不确定的环境中提供了独特的效率分解.
- 正如保险公司案例研究所显示的那样,ZNDEA方法是在模两可的条件下进行绩效评估的宝贵工具.
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