创新的直观的模糊的公平聚合运算符与基于线性编程的决策方法
Muhammad Riaz1, Hafiz Muhammad Athar Farid1, Rukhsana Kausar1
1Department of Mathematics, University of the Punjab, Lahore, Pakistan.
Journal of ambient intelligence and humanized computing
|June 8, 2023
概括
本研究为直观模糊集 (InFS) 引入了新的操作规则和聚合运算符,以改善不完全信息的决策. 开发的方法为多标准决策 (MCDM) 问题提供了公平的解决方案.
科学领域:
- 决策科学 决策科学
- 模糊逻辑系统 模糊逻辑系统
- 运营研究 运营研究
背景情况:
- 直觉模糊集合 (InFS) 理论为战略规划和决策提供了一个强大的框架,特别是在处理不确定性时.
- 聚合运营商 (AO) 对于决策过程中综合信息至关重要,但不完整数据带来了挑战.
- 现有的方法在有限信息的复杂决策环境中难以提供公平和中立的解决方案.
研究的目的:
- 在直观的模糊集环境中开发新的操作规则和聚合运算符.
- 引入一种公平的方法来处理直观的模糊信息,利用比例分布.
- 构建一个公平的多标准决策 (MCDM) 方法,使用拟议的运算符,并处理部分权重信息.
主要方法:
- 根据比例分配,为InFS制定新的运行法律.
- 为直观模糊数构建公平聚合运算符.
- 整合线性编程模型以确定具有部分信息的标准权重.
- 制定一个公平的MCDM方法,包括多个决策者的评估.
主要成果:
- 为InFS成功建立了新的操作规则和公平聚合运营商.
- 建立了一个公平的MCDM方法,在处理不完整的信息和部分权重方面表现出有效性.
- 拟议的方法为InFS环境中的决策挑战提供了中立或公平的补救措施.
- 严格的实施验证了开发的聚合运营商的有效性.
结论:
- 拟议的操作规则和聚合运算符在直观模糊集框架内增强了决策能力.
- 开发的公平的MCDM方法有效地解决了不完整信息和部分权重数据所带来的挑战.
- 这项研究为战略规划和不确定性下复杂决策分析提供了创新工具.
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