一种新的多属性决策方法,基于新的得分函数和混合加权得分测量在间隔值的费尔马特模糊环境中
Hongwu Qin1, Qiangwei Peng1, Xiuqin Ma1
1College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu China.
概括
本研究为区间值的费尔马特模糊集 (IVFFS) 引入了一个新的评分函数,通过克服现有方法的局限性来改善多属性决策 (MADM).
科学领域:
- 模糊的数学 模糊的数学
- 决策科学 决策科学
背景情况:
- 间隔值的费尔马特模糊集 (IVFFS) 提供了对不确定性的增强处理.
- 现有的多属性决策 (MADM) 方法在优先顺序和零误差除法方面存在局限性.
研究的目的:
- 为IVFFNs提出一个新的评分函数 (SCF).
- 开发一种新的MADM方法,利用SCF和混合加权得分测量.
- 为了解决区间值的费尔马特模糊环境中现有的MADM方法的局限性.
主要方法:
- 开发一个新的分数函数 (SCF) 用于区间值的费尔马特模糊数 (IVFFNs).
- 构建一个新的多属性决策 (MADM) 方法,整合SCF和混合加权得分指标.
- 通过三个案例研究验证,将拟议的方法与现有方法进行比较.
主要成果:
- 拟议的SCF有效地区分任何两个IVFFN.
- 新的MADM方法成功地克服了优先顺序和零误差划分的问题.
- 对比分析表明,拟议的方法产生了更高的识别指数和更低的错误率.
结论:
- 开发的MADM方法提供了在区间值的费尔马特模糊设置中进行决策的卓越方法.
- 新的评分功能增强了IVFFSs.s.内部的歧视力.
- 这项研究为涉及不确定性的复杂决策问题提供了可靠的解决方案.
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