一种新的方法,基于中性质的Bonferroni平均运算符,在多属性组决策中的梯形和三角形中性质间隔环境中
D Nagarajan1, A Kanchana1, Kavikumar Jacob2
1Department of Mathematics, Rajalakshmi Institute of Technology, Kuthambakkam, Chennai, India.
Scientific reports
|June 28, 2023
概括
这项研究引入了一种新的中性学多标准决策方法,使用梯形和三角形数字来提高复杂组决策的准确性. 该方法有效处理不确定性和主观数据,为供应商选择等现实问题提供实际解决方案.
科学领域:
- 决策科学 决策科学
- 运营研究 运营研究
- 模糊的逻辑和人工智能
背景情况:
- 中性学多标准分析涉及不完整或模两可的信息的决策.
- 现有的方法难以有效地捕捉不确定性和聚合主观偏好.
- 中性化多属性组决策 (NMAGDM) 需要强大的方法来处理各种数据类型.
研究的目的:
- 开发一种新的方法,用于使用梯形和三角形中性质数的中性质多属性组决策 (NMAGDM).
- 引入新方法来计算中微镜可能性度和平均值.
- 提出新的聚合运算符,特别是形和三角形中性波罗尼平均值 (TITRNBM) 和加权波罗尼平均值 (TITRNWBM) 运算符.
主要方法:
- 使用单值中性三角形和形数来表示决策者信息.
- 开发用于梯形和三角型中性质集合的新型中性质可能性度计算.
- 介绍和分析TITRNBM和TITRNWBM运算符用于偏好聚合.
- 提出基于TITRNWBM运营商和可能性程度的NMAGDM方法.
主要成果:
- 这项研究成功地定义和计算了形和三角形集合的中性形可能性度和平均值.
- 开发了新的聚合运算符 (TITRNBM和TITRNWBM),在处理不确定性方面提供了更高的灵活性和准确性.
- 拟议的NMAGDM方法通过制造供应商选择示例来证明其实际适用性和有效性.
结论:
- 开发的中性学决策框架提供了一个更准确和灵活的方法,用于在不确定性下处理复杂的团体决策.
- 在NMAGDM中,TITRNWBM操作员和可能性度方法为NMAGDM提供了显著的进步,特别是在涉及梯形和三角中性信息的问题上.
- 经过验证的示例证实了在现实场景中提出的战略的实际实用性和有效性.
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