基于球形模糊集的最佳-最差方法的扩展,用于多标准决策
Gholamreza Haseli1,2, Reza Sheikh3, Saeid Jafarzadeh Ghoushchi4
1Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, Mexico.
概括
本研究引入了一种新的球体模糊最佳最坏方法 (SF-BWM),用于不确定性下的多标准决策 (MCDM). 与现有方法相比,SF-BWM提供了更好的准确性和更好的一致性比率.
科学领域:
- 决策科学 决策科学
- 运营研究 运营研究
- 模糊的数学 模糊的数学
背景情况:
- 多标准决策 (MCDM) 通常涉及模两可的信息和模糊的判断,需要强大的工具来处理不确定性.
- 最好的最坏的方法 (BWM) 是一种强大的MCDM技术,它需要较少的对对比,并产生一致的结果.
- 现有的MCDM方法与定性判断的固有模糊性作斗争,影响解决方案的可靠性.
研究的目的:
- 开发使用球形模糊集 (SFS) 的最佳-最差方法 (BWM) 的扩展版本,以有效地解决以不确定性为特征的MCDM问题.
- 通过结合SFS提供的三维功能 (会员资格,非会员资格和犹程度) 来提高决策者更准确地表达判断的能力.
- 为拟议的球体模糊最佳最坏方法 (SF-BWM) 引入一个一致性比,以评估其可靠性.
主要方法:
- 开发一个具有非线性约束的优化模型来计算最佳球形模糊重量系数 (SF-BWM).
- 建议并应用SF-BWM的一致性比,以评估决策者判断的可靠性.
- 使用两个数值多标准决策问题验证SF-BWM的验证.
主要成果:
- SF-BWM 确定的标准权重与传统的 BWM 和模糊的 BWM 相同的优先顺序.
- 通过SF-BWM获得的标准重量值的差异表明结果的准确性和可靠性得到了提高.
- 与BWM和模糊BWM相比,提出的SF-BWM在一致性比率上显示出三倍的改进.
结论:
- 球形模糊最佳最坏方法 (SF-BWM) 有效处理MCDM问题的不确定性.
- 由于其优越的一致性比率,SF-BWM提供了更准确和可靠的结果.
- 球形模糊集的集成增强了MCDM决策的表达力和精度.
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