Back to Search
Start Over
Application of a Depth Model of Precise Matching between People and Posts Based on Ability Perception.
- Source :
- Computational Intelligence & Neuroscience; 9/22/2022, p1-10, 10p
- Publication Year :
- 2022
-
Abstract
- Under the modern environment, the reconstruction of enterprise's core competitiveness depends not only on capital and technical strength, but also on the overall strength of its human resources. At the same time, effective allocation and rational use of talents are needed to create good performance for enterprises. Enterprise human resource management is the key part of the whole enterprise management. At the same time, it is also a necessary preparation for the continuous development and innovation of enterprises. In the whole process of human resource management, the core work is person-post matching. Only by promoting the reasonable implementation of person-post matching can other management work be carried out smoothly. This paper expounds two major elements in human resource management, namely, the concept and measurement of person-post matching and the principle of person-post matching. And the factors in the matching of people and posts are analyzed. This paper probes into the implementation of person-post matching in enterprise human resource management. Based on this, this paper puts forward a depth model of accurate matching between people and posts based on ability perception. On the basis of studying the optimization of human resource scheduling, this paper takes into account three factors: resource constraints, heterogeneity of employee efficiency and time sequence relationship, and uses integer linear programming theory to model the system with the shortest construction period as the goal. The research shows that the accuracy of this algorithm can reach about 94%, which is about 8% higher than the traditional algorithm. It has certain superior performance. This will provide some reference for related researchers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16875265
- Database :
- Complementary Index
- Journal :
- Computational Intelligence & Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 159271092
- Full Text :
- https://doi.org/10.1155/2022/9040349