为多个少数群体子组和多个标准设计pareto-optimal选择系统
Wilfried De Corte1, Paul R Sackett2, Filip Lievens3
1Faculty of Psychology, Department of Data-Analysis, Ghent University.
本研究介绍了多目标帕雷托最佳 (PO) 选择系统,以平衡多种多样性和质量标准. 新的方法有效地揭示了所有符合条件的PO选择设计,并捕捉了不同劳动力的权衡.
科学领域:
- 组织心理学 组织心理学
- 决策科学 决策科学 决策科学
- 人力资源管理 人力资源管理
背景情况:
- 目前选择系统的帕雷托最佳 (PO) 方法仅限于一个少数群体和一个标准.
- 增加工作场所多样性和多重选择标准需要先进的方法.
- 现有的方法无法在复杂的选择场景中捕捉出交易权衡的全部范围.
研究的目的:
- 扩展现有的PO选择系统设计方法,以处理多个标准和多个少数群体.
- 为平衡质量和多样性目标,制定混合多目标PO方法.
- 提出协助设计人员从多个目标中选择最佳系统的程序.
主要方法:
- 开发了一种混合多目标PO方法来计算选择系统,以平衡多个质量和多样性目标.
- 提出了三种二维子空间程序,以协助在PO系统中进行选择.
- 这些方法用示例应用程序来说明,并通过交叉验证研究来验证.
主要成果:
- 新的多目标PO方法揭示了符合条件的PO选择设计的完整套.
- 这些方法忠实地捕捉了帕雷托权衡前线的两个以上的目标.
- 经过验证的PO选择设计在新样本中显示出对替代方法的优势.
结论:
- 开发的多目标PO选择系统有效地解决了多样化的劳动力和多重选择标准的复杂性.
- 提出的方法为设计公平和有效的选择系统提供了一个全面的框架.
- 可执行代码可用,以促进这些先进的PO方法的实施.
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