Linkage Mechanism of Employee Performance Optimization and Functional Textile Quality Control Based on Improved DDQN
Main Article Content
Abstract
To address the challenge of jointly optimizing employee performance and functional textile quality in intelligent manufacturing environments, this study proposes a multi-objective collaborative optimization framework based on an improved Double Deep Q-Network (DDQN). A multidimensional state-space model is constructed by integrating employee behavioral indicators, production process parameters, and real-time quality inspection data, enabling coupled representation of human–machine interactions and manufacturing performance. The proposed method incorporates a dynamic reward function, prioritized experience replay, and multi-step temporal difference prediction to improve policy learning efficiency and long-term decision stability. Considering that modern textile manufacturing increasingly relies on heterogeneous sensing devices and distributed industrial information acquisition, the proposed state modeling and decision-making framework provides a scalable architecture for integrating multimodal production data and supporting intelligent monitoring systems. Experimental evaluations demonstrate that the improved DDQN reduces the average defect rate to 2.6% with a standard deviation of 0.4 while achieving a comprehensive performance score of 89.4, significantly outperforming conventional DDQN and static scheduling strategies. The framework effectively balances production efficiency, operational consistency, and quality stability, offering a practical solution for intelligent textile manufacturing and providing methodological insights for future interdisciplinary research on advanced sensing, industrial information fusion, and smart manufacturing infrastructures.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
H. Hatif, K. L. Lee, and G. Nawanir, “Improving employee and workplace performance via lean manufacturing practices: a case study in textile company,” International Journal of Industrial Management, vol. 18, no. 1, pp. 43-59, 2024, doi: 10.15282/ijim.18.1.2024.10425.
M. Ewnetu and Y. Gzate, “Assembly operation productivity improvement for garment production industry through the integration of lean and work-study, a case study on Bahir Dar textile share company in garment, Bahir Dar, Ethiopia,” Heliyon, vol. 9, no. 7, pp. e17917, 2023, doi: 10.1016/j.heliyon.2023.e17917.
M. R. Islam, M. Z. H. Zamil, M. E. Rayed, M. M. Kabir, M. F. Mridha, S. Nishimura, et al., “Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes,” IEEE Access, vol. 12, pp. 121449-121479, 2024, doi: 10.1109/ACCESS.2024.3453664.
M. P. Sikka, A. Sarkar, and S. Garg, “Artificial intelligence (AI) in textile industry operational modernization,” Research Journal of Textile and Apparel, vol. 28, no. 1, pp. 67-83, 2024, doi: 10.1108/RJTA-04-2021-0046.
L. Chen, Q. Wang, C. Deng, B. Xie, X. Tuo, and G. Jiang, “Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots,” Sensors, vol. 24, no. 7, pp. 2061, 2024, doi: 10.3390/s24072061.
S. Zhang, L. Zhang, F. Xu, S. Cheng, W. Su, and S. Wang, “Dynamic deployment method based on double deep Q-network in UAV-assisted MEC systems,” Journal of Cloud Computing, vol. 12, no. 1, pp. 130, 2023, doi: 10.1186/s13677-023-00507-6.
Y. Li, C. Yu, M. Shahidehpour, T. Yang, Z. Zeng, and T. Chai, “Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects,” Proceedings of the IEEE, vol. 111, no. 9, pp. 1055-1096, 2023, doi: 10.1109/JPROC.2023.3303358.
N. Yang, S. Chen, H. Zhang, and R. Berry, “Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, Its Applications, and Future Research Trajectories,” IEEE Communications Surveys & Tutorials, vol. 27, no. 1, pp. 546-594, 2025, doi: 10.1109/COMST.2024.3405075.
J. Zhou, Y. Tian, and M. Ren, “Data-efficient reinforcement learning by generalized value estimation,” Machine Learning, vol. 114, no. 6, pp. 133, 2025, doi: 10.1007/s10994-025-06763-8.
L. Zhao, T. Chang, L. Guo, J. Zhang, L. Zhang, and J. Ma, “An Overestimation Reduction Method Based on the Multistep Weighted Double Estimation Using Value-Decomposition Multi-agent Reinforcement Learning,” Neural Processing Letters, vol. 56, no. 3, pp. 152, 2024, doi: 10.1007/s11063-024-11611-2.
D. Hussain and M. C. Figueiredo, “Improving the time-based performance of the preparatory stage in textile manufacturing process with value stream mapping,” Business Process Management Journal, vol. 29, no. 3, pp. 801-837, 2023, doi: 10.1108/BPMJ-08-2022-0366.
P. Kalia, M. Singla, and R. Kaushal, “Human resource management practices and employee retention in the Indian textile industry,” International Journal of Productivity and Performance Management, vol. 73, no. 11, pp. 96-121, 2024, doi: 10.1108/IJPPM-01-2022-0057.
J. M. X. Casavilca, B. A. Y. Pedraza, C. B. A. Meneses, and R. J. A. Zapana, “Human talent management and the work performance of employees in a textile company in Comas,” Southern Perspective / Perspectiva Austral, vol. 1, pp. 5, 2023, doi: 10.56294/pa20235.
Y. Liu, C. Zhang, and X. Dong, “A survey of real-time surface defect inspection methods based on deep learning,” Artificial Intelligence Review, vol. 56, no. 10, pp. 12131-12170, 2023, doi: 10.1007/s10462-023-10475-7.
S. Zhou, J. Zhao, Y. Shi, Y. Wang, and S. Mei, “Research on improving YOLOv5s algorithm for fabric defect detection,” International Journal of Clothing Science and Technology, vol. 35, no. 1, pp. 88-106, 2023, doi: 10.1108/IJCST-11-2021-0165.
A. Metin and T. T. Bilgin, “Automated machine learning for fabric quality prediction: a comparative analysis,” PeerJ Computer Science, vol. 10, pp. e2188, 2024, doi: 10.7717/peerj-cs.2188.
T. Liang, T. Liu, J. Wang, J. Zhang, and P. Zheng, “Causal deep learning for explainable vision-based quality inspection under visual interference,” Journal of Intelligent Manufacturing, vol. 36, no. 2, pp. 1363-1384, 2025, doi: 10.1007/s10845-023-02297-9.
T. Meeradevi, S. Sasikala, S. Gomathi, and K. Prabakaran, “An analytical survey of textile fabric defect and shade variation detection system using image processing,” Multimedia Tools and Applications, vol. 82, no. 4, pp. 6167-6196, 2023, doi: 10.1007/s11042-022-13575-8.
R. S. Sabeenian, E. Paul, and C. Prakash, “Fabric defect detection and classification using modified VGG network,” The Journal of The Textile Institute, vol. 114, no. 7, pp. 1032-1040, 2023, doi: 10.1080/00405000.2022.2105112.
X. Luo, “Reshaping Coordination Efficiency in the Textile Supply Chain Through Intelligent Scheduling Technologies,” Economics and Management Innovation, vol. 2, no. 4, pp. 1-9, 2025, doi: 10.71222/ww35bp29.
M. Chen, Y. Kang, K. Li, P. Li, and Y. Zhao, “Deep reinforcement learning for maintenance optimization of multicomponent production systems considering quality and production plan,” Quality Engineering, vol. 37, no. 2, pp. 219-230, 2025, doi: 10.1080/08982112.2024.2373362.
O. Nocentini, J. Kim, Z. M. Bashir, and F. Cavallo, “Learning-based control approaches for service robots on cloth manipulation and dressing assistance: a comprehensive review,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, pp. 117, 2022, doi: 10.1186/s12984-022-01078-4.
H. Xu, J. Zheng, L. Huang, J. Tao, and C. Zhang, “Solving Dynamic Multi-Objective Flexible Job Shop Scheduling Problems Using a Dual-Level Integrated Deep Q-Network Approach,” Processes, vol. 13, no. 2, pp. 386, 2025, doi: 10.3390/pr13020386.
A. B. Mizhir, “Analysis of the Impact of Implementation of Integrated Manufacturing Information Systems (MES) in Increasing Production Efficiency in the Electronics Industry,” Jurnal Mekintek: Jurnal Mekanikal, Energi, Industri, Dan Teknologi, vol. 16, no. 1, pp. 9-18, 2025, [Online]. Available: https://www.ejournal.isha.or.id/index.php/Mekintek/article/view/377.
M. M. Mohamed and A. S. Alraddadi, “Agile Factor Flexible Industry 4.0 with MES Manufacturing Execution System Along with ERP Back-End Integration: Ready-Made Garments as a Case Study,” American Journal of Operations Research, vol. 14, no. 4, pp. 137-150, 2024, doi: 10.4236/ajor.2024.144007.
Y. Chen, W. Chiu, Q. Wang, J. Chen, and H. Zhao, “Energy-Efficient Prediction in Textile Manufacturing: Enhancing Accuracy and Data Efficiency WITH Ensemble Deep Transfer Learning,” IEEE Access, vol. 13, pp. 57177-57190, 2025, doi: 10.1109/ACCESS.2025.3551798.
A. A. Amin, N. Alvi, M. Irfan, M. Abdullah, and H. U. Manzoor, “Design of a Low-Cost Efficient IoT Based SCADA System for Automating Different Textile Machines and Controllers for Central Monitoring,” The Journal of Engineering, vol. 2025, no. 1, pp. e70089, 2025, doi: 10.1049/tje2.70089.
F. He, B. Xin, Z. Zhan, W. Yang, and J. Liu, “A review of fabric defect detection in textile manufacturing,” The Journal of The Textile Institute, pp. 1-19, 2025, doi: 10.1080/00405000.2025.2502188.
D. Zhao, Z. Ding, W. Li, S. Zhao, and Y. Du, “Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems,” Applied Sciences, vol. 14, no. 2, pp. 851, 2024, doi: 10.3390/app14020851.
J. Chang, D. Yu, Z. Zhou, W. He, and L. Zhang, “Hierarchical Reinforcement Learning for Multi-Objective Real-Time Flexible Scheduling in a Smart Shop Floor,” Machines, vol. 10, no. 12, pp. 1195, 2022, doi: 10.3390/machines10121195.
L. Song, Y. Li, and J. Xu, “Dynamic Job-Shop Scheduling Based on Transformer and Deep Reinforcement Learning,” Processes, vol. 11, no. 12, pp. 3434, 2023, doi: 10.3390/pr11123434.