综合的直觉模糊网络数据包裹分析,包括不必要的输出和共享资源
Mohammad Aqil Sahil1, Q M Danish Lohani1
1South Asian University, Maidan Garhi, New Delhi, 110068, Delhi, India.
MethodsX
|April 25, 2024
概括
这项研究通过使用新型网络数据环境分析 (DEA) 模型来增强银行效率分析. 它通过模糊的数字来解决不确定性,为银行提供更好的决策.
科学领域:
- 运营研究 运营研究
- 财务管理 财务管理
- 应用数学 应用数学 应用数学
背景情况:
- 银行业的效率对于经济增长至关重要.
- 传统的数据包装分析 (DEA) 在处理内部流程和不准确数据方面存在局限性.
- 现有的模型通常将决策单元 (DMU) 视为黑子.
研究的目的:
- 开发一个全面的网络两阶段的DEA模型来评估银行效率.
- 将共享的投入,不必要的输出和外部因素纳入效率评估中.
- 将模型扩展到直觉模糊环境,以管理数据不确定性.
主要方法:
- 开发了一个网络两阶段的DEA模型,共享输入,中间措施,不良输出和外部因素.
- 该模型使用抛物线直观模糊数扩展,以处理更高阶的不精确数据集.
- 该方法用一个说明性的例子进行了验证,并与现有方法进行了比较.
主要成果:
- 拟议的网络双阶段DEA模型有效地评估了银行系统的效率.
- 使用抛物线直观的模糊数字成功地解决了效率测量的不确定性.
- 该方法提供了最佳的效率值,以改善决策.
结论:
- 开发的网络双阶段DEA模型为银行部门的效率评估提供了更现实的,更全面的方法.
- 对印度公共部门银行的应用证明了拟议方法的实际实用性和有效性.
- 这项研究为金融决策者提供了宝贵的见解,以优化银行业务.
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