Approach of Job-person Matching Method in Manufacturing Enterprises Based on AHP-GA-BP
Sen Wang, Qianrui Dai, Aoyue Ma, Xudong Hong, Weipeng Tai · 2024
In modern manufacturing enterprises, the issue of temporary leave and job rotation of frontline production employees affects production scheduling. To ensure safety and production continuity, companies often use substitute workers. However, due to the subjectivity of managers, the substitute workers may not have the required knowledge, skills, and abilities as the requirements of the position. To address this issue, an AHP-GA-BP (Analytic Hierarchy Process-Genetic Algorithm-Backpropagation Neural Network) model is proposed. This model is based on the KSAO (knowledge, skills, abilities, other indicates that can have an impact on work) model and combines the Analytic Hierarchy Process (AHP), Genetic Algorithms (GA), and Backpropagation Neural Network (BP). AHP is used to evaluate and rank the characteristics, obtaining accurate and reasonable weights for the features. Then, the sample data is further optimized and input into the GA-BP model for training. The model is trained and tested using real data from a digital system in a certain process manufacturing company. Experimental results show that the AHP-GA-BP model achieves a 20.09% and 74.85% improvement in prediction accuracy compared to the GA-BP model and BP neural network, respectively, in this specific task.