Application of a Depth Model of Precise Matching between People and Posts Based on Ability Perception

Area of Science:

• Human Resource Management
• Organizational Psychology
• Operations Research

Background:

• Enterprise competitiveness relies on human resources, necessitating effective talent allocation.
• Human resource management is crucial for enterprise development and innovation.
• Person-post matching is the core of human resource management for smooth operations.

Purpose of the Study:

• To analyze the concept, measurement, principles, and factors of person-post matching.
• To develop an accurate person-post matching model based on ability perception.
• To optimize human resource scheduling using integer linear programming.

Main Methods:

• Analysis of person-post matching concepts and principles.
• Development of a depth model for accurate person-post matching using ability perception.
• Application of integer linear programming for human resource scheduling optimization, considering resource constraints, employee efficiency heterogeneity, and time sequences.

Main Results:

• The proposed depth model enhances the accuracy of person-post matching.
• The integer linear programming model optimizes human resource scheduling with a goal of shortest construction period.
• The developed algorithm achieved approximately 94% accuracy, an 8% improvement over traditional methods.

Conclusions:

• Accurate person-post matching is vital for enterprise competitiveness and innovation.
• The ability perception-based model offers a more precise approach to matching individuals with suitable positions.
• Optimized human resource scheduling using integer linear programming demonstrates superior performance, providing valuable insights for researchers.