研究生和主管匹配决策,考虑基于TOPSIS和灰色相关度的基于稳定性的公平性
Xiaohua Liu1, Qi Yue2, Bin Hu3
1School of Management, Shanghai University of Engineering Science, Shanghai, 201620, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的方法,用于将研究生与主管匹配起来,考虑学生的偏好和合规心理学,以获得公平而稳定的任务. 该方法确保在学术实习中获得最佳匹配结果.
科学领域:
- 决策科学 决策科学
- 人工智能的人工智能
- 教育管理的教育管理.
背景情况:
- 有限的研究存在于研究生和主管匹配 (GSSM) 的决策过程.
- 现有的GSSM方法往往忽略了关键因素,如合规心理学和个人学生偏好.
- 公平性和稳定性在学术匹配中至关重要,但在当前的模式中没有充分解决这些问题.
研究的目的:
- 为研究生和监督人员 (GSS) 提出一种新的多对一匹配决策方法.
- 将符合性心理学和研究生偏好纳入GSSM过程.
- 确保在研究生学生和导师的匹配结果中以稳定性为基础的公平性.
主要方法:
- 将语言术语矩阵 (LTM) 转换为毕达哥拉斯模糊矩阵 (PFM).
- 根据研究生学生转移关系开发了一种符合系数矩阵.
- 使用TODIM和TOPSIS方法,以及灰色相关度,构建全面的偏好和满意度矩阵.
- 建立了许多对一个的GSSM模型,转变为一个对一个的模型以实现最佳匹配.
主要成果:
- 提出了一种针对毕达哥拉斯模糊数 (PFN) 的新评分方法.
- 开发了一种有效的方法来将语言术语集 (LTS) 转换为 PFN.
- 引入了一种改进的权重计算,将符合性心理学与最佳最差方法 (BWM) 结合起来.
- 介绍了一种新的满意度计算方法,该方法结合了符合性心理学和研究生偏好.
- 成功构建并验证了一种确保基于稳定性的公平性的GSSM模型.
结论:
- 拟议的GSSM方法通过整合心理和偏好因素,有效地解决了现有模型的局限性.
- 开发的模型提供了一种可行,有效和创新的解决方案,以实现稳定和公平的研究生学生监督任务.
- 该研究为学术界复杂的匹配问题提供了模糊数学和决策的新技术.
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