Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF)...

Area of Science:

• Human Resource Management
• Big Data Analytics
• Artificial Intelligence in HR

Background:

• Human-post matching big data is crucial in modern enterprises.
• State-owned enterprises face prominent shortcomings in human-post matching big data due to reforms.
• Existing methods lack accuracy in evaluating human-job fit within state-owned enterprises.

Purpose of the Study:

• To address challenges in state-owned enterprise big data related to human-post matching.
• To develop an effective method for accurately evaluating human-job matching.
• To provide a scientific basis for rational talent and resource allocation decisions.

Main Methods:

• Development of a big data model for human-post matching evaluation using Radial Basis Function (RBF) neural networks.
• Comprehensive application of the RBF neural network combined with the Analytic Hierarchy Process (AHP) method.
• Utilizing AHP to determine weights of evaluation indices and RBF's self-adaptive learning to mitigate AHP subjectivity.

Main Results:

• Scientifically and effectively evaluates the matching degree between personnel qualifications/abilities and job requirements.
• Facilitates timely personnel adjustments to maximize human resource efficiency.
• Combines strengths of AHP and RBF neural networks to enhance evaluation convenience and effectiveness.

Conclusions:

• The integrated RBF neural network and AHP model offers a robust solution for human-post matching in state-owned enterprises.
• This approach improves the accuracy of talent assessment and resource allocation.
• The model supports dynamic adjustments in personnel placement, boosting overall organizational efficiency.