数据不确定性下的双阶段网络系统的马尔姆奎斯特生产率指数:现实世界的案例研究
Seyed Ehsan Shojaie1, Seyed Jafar Sadjadi2, Reza Tavakkoli-Moghaddam3
1Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
PloS one
|July 18, 2024
概括
本研究引入了一种新的方法,通过将马尔姆奎斯特生产率指数 (MPI) 与不确定的编程相结合,在双阶段网络数据包裹分析 (TSNDEA) 中测量生产率变化. 这种方法通过考虑数据不确定性来提高绩效评估.
科学领域:
- 运营研究 运营研究
- 管理科学 管理科学
- 计量经济学 计量经济学
背景情况:
- 衡量生产率变化对于决策单位 (DMU) 绩效评估至关重要.
- 传统的数据包裹分析 (DEA) 模型与数据不确定性作斗争.
- 在金融和保险领域,双阶段网络结构很常见.
研究的目的:
- 开发一种新的方法来测量数据不确定性下的生产力变化.
- 将马尔姆奎斯特生产率指数 (MPI) 与双阶段网络数据包裹分析 (TSNDEA) 整合起来.
- 提高DMU生产率测量的稳定性和可靠性.
主要方法:
- 使用双阶段网络数据包裹分析 (TSNDEA).
- 整合马尔姆奎斯特生产率指数 (MPI) 与一个不确定的规划框架.
- 使用模糊的数学编程来模拟DEA中的数据不确定性.
主要成果:
- 提出的方法有效地通过考虑技术效率和技术进步来衡量生产率的变化.
- 模糊编程的集成增强了不确定的输入和输出数据的建模.
- 该方法提供了更强大和可靠的生产率变化评估.
结论:
- 开发的方法提供了对DMU生产率变化的全面分析.
- 它帮助决策者确定提高效率的领域.
- 这些发现支持更明智的资源配置和战略决策.
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