GRA-RF组合算法在农信金融企业工资要素优选及测算中的应用

doi:10.16381/j.cnki.issn1003-207x.2021.0108

摘要/Abstract

摘要： 工资是影响员工行为的最敏感因素，适时推动工资制度优化对稳定人才队伍、提高工作效率、提升组织效能具有重要作用。业界在工资制度设计层面的研究较多，但涉及工资要素优选及测算的方法研究较少。本文以作者研究团队完成的40余家农信金融企业管理咨询项目成果为案例，将智能算法引入工资测算领域，首先运用灰色关联分析对企业样本数据预处理，在多种工资要素中筛选出最优特征变量，再使用随机森林模型对员工工资进行预测，同时基于相同的训练样本进行多种预测模型的对比；为了检验预测工资的适用性，通过相关性分析，验证工资策略与组织效能的重要关联。本文所提出的工资制度设计与优化的智能算法，可以确定工资影响因素的最优权重组合，为农信金融企业工资改革所借鉴，也可以为其他银行类金融企业的工资制度设计提供算法参考。

关键词: 农信金融企业, 工资要素优选, 工资测算, 随机森林, 灰色关联分析, GRA-RF组合算法

Key words: rural credit financial enterprises, optimization of wage elements, wage calculation, random forest, grey relational analysis

中图分类号:

F404.2
F244

余顺坤, 宋宇晴. GRA-RF组合算法在农信金融企业工资要素优选及测算中的应用[J]. 中国管理科学, 2022, 30(4): 86-95.

YU Shun-kun, SONG Yu-qing. Research on the Application of GRA-RF in Optimization and Calculation of Age Elements of Rural Credit Financial Enterprise[J]. Chinese Journal of Management Science, 2022, 30(4): 86-95.

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