使用决策算法对管理会计信息进行评估,并以循环式封闭的模糊的CRITIC WASPAS模型中优先权重进行权衡
1Hunan Polytechnic of Environment and Biology, Hengyang, 421005, Hunan, China. Lqp11328@163.com.
Scientific reports
|December 21, 2025
概括
本研究引入了一种新型的循环固定模糊 (CFF) 框架,用于管理会计中的不确定性. 在这套CFF中使用的CRITIC-WASPAS技术可以改善决策,提高运营效率和风险管理.
科学领域:
- 决策科学 决策科学
- 管理科学 管理科学
- 模糊的集合理论 模糊的集合理论
背景情况:
- 管理会计包括处理财务和非财务数据,用于内部决策.
- 数据的不确定性给风险管理和运营效率带来了挑战.
- 模糊集合论提供了处理不准确和不确定的信息的方法.
研究的目的:
- 开发使用模糊集的决策算法,以增强管理会计.
- 将Critic和WASPAS方法集成到一个循环式模糊 (CFF) 框架中.
- 解决不确定性下的多属性小组决策问题.
主要方法:
- 使用圆形固态模糊集 (CFFS) 来模拟与会员和非会员等级的不确定性.
- 通过标准间相关性 (CRITIC) 方法扩展了标准的重要性,用于属性加权.
- 应用权重总和产品评估 (WASPAS) 方法进行决策.
- 在CFF框架内使用CRITIC-WASPAS开发了一个多属性组决策算法.
主要成果:
- 拟议的基于CFF的CRITIC-WASPAS模型有效地处理管理会计数据中的不确定性.
- 一个案例研究表明,该模型能够在多个选项中选择最佳替代方案.
- 敏感性和比较分析证实了该模型的有效性,可靠性和性能.
结论:
- 基金框架与CRITIC-WASPAS相结合,为管理会计的决策提供了一个强大的方法.
- 这种方法通过解决数据不确定性,提高了风险管理和运营效率.
- 该研究验证了拟议模型的实际适用性和优于现有方法的优势.
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