摘要
人才政策系统的"复杂性"体现在对其要素评价结果难以量化,系统优化方向与需要与人才社会心理需求对应等方面。为解决上述问题,该文以陕西省企业人才政策系统为例,运用实证调查方法收集人才对政策要素的评价数据,同时采用混沌粒子群优化算法(CPSO)建立人才政策满意度、敬业度和流失倾向三者间的关系模型,分析各政策要素对三者的"贡献度",作为确定陕西企业人才政策系统优化路径的依据。仿真结果表明,与传统的回归方法构建人才政策体系的数学模型相比,CPSO方法精确度高、计算过程便捷,可以推广应用到其他管理系统优化中。
The difficulty of talent policy system optimization is that the evaluation results of policy factors are hard to quantify, and exact correspondence between the system optimization direction and the social and psy- chological requirements of talents, etc. In order to solve the problems, this paper takes Shaanxi Province tal- ent policy system as example, using empirical survey method to collect data about talent policy satisfaction, engagement and demission tendency. Based on the questionnaire data, the chaotic particle swarm optimization （CPSO） algorithm is used to build the relationship model for talent policy system. The contribution rate of tal- ents policy for the policy satisfaction, engagement and the demission tendency can be obtained, as a basis for determining Shaanxi Province enterprise talents policy system optimization. The simulation results show that, compared with the traditional regression approach to build the mathematical model of the talent policy system, CPSO method has high accuracy and low complexity for computer realization and can be extended to the opti- mization of other supervisory systems.
出处
《西北大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第4期519-525,共7页
Journal of Northwest University（Natural Science Edition）
基金
陕西省自然科学基础研究计划基金资助项目(2014JM2713)
关键词
政策系统
满意度
敬业度
流失倾向
混沌粒子群
policy system
satisfaction
engagement
demission tendency
chaotic particle swarm optimization
