使用PL-BWM和概率语言三向TOPSIS方法进行多指数决策:一个案例研究
Jie Chen1,2
1School of Statistics and Data Science, Qufu Normal University, Qufu, 273165, China. jiechen6688@163.com.
Scientific reports
|March 13, 2025
概括
本研究引入了一种新的决策系统,使用概率语言术语集 (PLTS) 来处理复杂的选择. 通过结合情绪因素和三方决策方法,增强的方法提高了选择最佳绿色供应商的准确性.
科学领域:
- 决策科学 决策科学
- 运营研究 运营研究
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 概率语言术语集 (PLTS) 在表达不确定的偏好方面提供了灵活性.
- 现有的多指数决策方法可能在捕捉决策者的情绪和排名方案方面缺乏精确性.
- 对于复杂的选择问题,如绿色供应商选择,需要强大的方法至关重要.
研究的目的:
- 为多指数问题开发基于PLTS的新型决策系统.
- 将决策者的情感因素整合到一个规范化的概率语言术语元素模型中.
- 为改进方案排名和选择提出一种概率语言三向TOPSIS方法.
主要方法:
- 开发一个规范化的概率语言术语元素模型,结合情感因素.
- 引入一个概率语言最佳-最差方法 (PL-BWM) 用于索引权重.
- 整合Jensen-Shannon差异,以结合主观和客观权重.
- 将TOPSIS扩展到使用中位评价参考点的三向决策模型.
主要成果:
- 拟议的PL-BWM准确地反映了决策者的偏好和指数的重要性.
- 与双向TOPSIS相比,概率语言三向TOPSIS方法克服了方案排名中的模糊性.
- 开发的系统证明了逻辑的一致性,并在绿色供应商选择案例研究中提供了卓越的结果.
结论:
- 这种新的决策系统有效地解决了语言信息不确定性的多指数问题.
- 结合情感因素和三方决策框架,提高了决策的准确性和可靠性.
- 提出的方法为复杂的选择任务提供了更合适和更稳定的方法.
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