扩展的DEMATEL方法与直观的模糊信息:电动汽车的案例
1School of Economics & Management, South China Normal University, Guangzhou, China.
PloS one
|December 19, 2024
概括
这项研究增强了决策试验和实验室 (DEMATEL) 方法,以处理复杂系统的不确定,直观的模糊数据. 改进的方法确定了电动汽车电池技术中的关键因果因素.
科学领域:
- 决策分析 决策分析
- 模糊的数学 模糊的数学
- 系统工程是系统工程.
背景情况:
- 传统的决策试验和实验室 (DEMATEL) 方法依赖于精确的数据,这不足以捕捉决策者的不确定性.
- 现实世界的决策通常涉及模糊和不确定的信息,需要更强大的分析方法.
- 现有的方法很难表现出复杂系统分析中固有的细微判断.
研究的目的:
- 扩展DEMATEL的方法,有效地分析直观的模糊信息.
- 开发一个框架来处理复杂系统中的主观和不确定的因素相关性.
- 提高DEMATEL在数据不准确的场景中的准确性和适用性.
主要方法:
- 结合直觉模糊集来表示决策者的判断和因素相关性.
- 使用专业运算符集成直觉模糊相关信息.
- 用直觉模糊矩阵运算来应用因子显著性和分类.
- 基于直觉模糊分析的因果关系图的构建.
主要成果:
- 成功地调整了DEMATEL来处理直观的模糊数字,解决数据不确定性.
- 在电动汽车 (EV) 电池技术中确定了四个主要原因因素和六个影响因素.
- 开发了一个因果关系图,说明了电动汽车电池环境中的因素相互依赖.
结论:
- 增强的DEMATEL方法提供了更现实的分析复杂的系统与不确定的数据.
- 在电动汽车电池技术中发现的因素为行业发展提供了可操作的见解.
- 这种方法有助于电动汽车公司为电池技术的进步做出战略决策.
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