Research on the Efficiency Improvement Model of Lighthouse Factory Operation Based on XGBoost Algorithm
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Abstract
This study proposes an operational efficiency improvement model for Lighthouse Factories based on the XGBoost algorithm. By integrating large-scale heterogeneous data from PLCs, MES, SCADA systems, maintenance records, and energy monitoring platforms, the model identifies operational inefficiencies and predicts critical production events through advanced feature engineering and cost-sensitive learning strategies. A comprehensive data-to-decision framework is established, enabling predictive maintenance, dynamic scheduling, and near real-time decision support. Experimental evaluation on an 18-month industrial dataset containing over 15 million records demonstrates superior performance compared with conventional machine learning approaches, achieving higher predictive accuracy, reduced unplanned downtime, and improved production throughput. The proposed framework is particularly applicable to Industry 4.0 environments supported by industrial wireless communication networks, antenna-enabled Industrial Internet of Things (IIoT) infrastructures, and cyber-physical manufacturing systems, where reliable data acquisition and transmission are essential for intelligent operation. The results confirm the effectiveness, scalability, and practical value of the proposed model for enhancing operational efficiency in smart manufacturing environments.
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References
A. Deligiannis, C. Argyriou, and D. Kourtesis, “Building a Cloud-based Regression Model to Predict Click-through Rate in Business Messaging Campaigns,” International Journal of Modeling and Optimization, vol. 10, no. 1, pp. 26-31, 2020, doi: 10.7763/IJMO.2020.V10.742.
A. T. Sufian, B. M. Abdullah, and O. J. Miller, “Smart manufacturing application in precision manufacturing,” Applied Sciences, vol. 15, no. 2, pp. 915, 2025, doi: 10.3390/app15020915.
R. Sun and Y. Ren, “A multi-source heterogeneous data fusion method for intelligent systems in the Internet of Things,” Intelligent Systems with Applications, vol. 23, Art. no. 200424, 2024, doi: 10.1016/j.iswa.2024.200424.
M. Altalhan, A. Algarni, and M. T. H. Alouane, “Imbalanced data problem in machine learning: A review,” IEEE Access, vol. 13, pp. 13686-13699, 2025, doi: 10.1109/ACCESS.2025.3531662.
S. S. Mansaray, X. Hongyi, and I. A. Sawaneh, “Assessing and enhancing operational efficiency in Sierra Leone’s retail banking sector: A comparative analysis using CCR and BCC DEA models,” Managerial and Decision Economics, vol. 45, no. 6, pp. 3705-3715, 2024, doi: 10.1002/mde.4225.
X. Li, J. Yu, J. Shaw, and Y. Wang, “Route-Level Transit Operational-Efficiency Assessment with a Bootstrap Super-Data-Envelopment Analysis Model,” Journal of Urban Planning and Development, vol. 143, no. 3, Art. no. 04017007, 2017, doi: 10.1061/(asce)up.1943-5444.0000388.
N. N. Dalei and J. M. Joshi, “Operational efficiency assessment of oil refineries using data envelopment analysis and Tobit model: Evidence from India,” International Journal of Energy Sector Management, vol. 17, no. 3, pp. 437-454, 2023, doi: 10.1108/ijesm-07-2020-0024.
S. Mital, R. L. Witman, J. Gibson, J. Huffman, and H. Miller, “Providing a User Extensible Service-Enabled Multi-Fidelity Hybrid Cloud-Deployable SoS Test and Evaluation (T&E) Infrastructure: Application of Modeling and Simulation (M&S) as a Service (MSaaS),” Information, vol. 14, no. 10, pp. 528, 2023, doi: 10.3390/info14100528.
K. Shivashankar, G. S. A. Hajj, and A. Martini, “Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review,” arXiv preprint arXiv:2504.11079, doi: 10.48550/arXiv.2504.11079.
J. Song, B. Wang, and X. Hao, “Optimization algorithms and their applications and prospects in manufacturing engineering,” Materials, vol. 17, no. 16, pp. 4093, 2024, doi: 10.3390/ma17164093.
N. Ejaz and S. Choudhury, “A Comprehensive Survey of Linear, Integer, and Mixed-Integer Programming Approaches for Optimizing Resource Allocation in 5G and Beyond Networks,” arXiv preprint arXiv:2502.15585, 2025, doi: 10.48550/arXiv.2502.15585.
K. Zehra, N. H. Mirjat, S. A. Shakih, K. Harijan, L. Kumar, and M. El Haj Assad, “Optimizing auto manufacturing: A holistic approach integrating overall equipment effectiveness for enhanced efficiency and sustainability,” Sustainability, vol. 16, no. 7, pp. 2973, 2024, doi: 10.3390/su16072973.
A. Santana, P. S. L. P. Afonso, A. Zanin, and R. A. Wernke, “Costing models for capacity optimization in Industry 4.0: Trade-off between used capacity and operational efficiency,” Procedia Manufacturing, vol. 13, pp. 1183-1190, 2017, doi: 10.1016/j.promfg.2017.09.193.
L. Lucantoni, “Data-driven methodologies for Predictive Maintenance and TPM implementation: Research approaches and applications,” 2023, [Online]. Available: https://iris.univpm.it/handle/11566/315249.
D. K. Yadav, A. Kaushik, and N. Yadav, “Predicting machine failures using machine learning and deep learning algorithms,” Sustainable Manufacturing and Service Economics, vol. 3, Art. no. 100029, 2024, doi: 10.1016/j.smse.2024.100029.
A. A. Tulbure, A. A. Tulbure, and E. H. Dulf, “A review on modern defect detection models using DCNNs–Deep convolutional neural networks,” Journal of Advanced Research, vol. 35, pp. 33-48, 2022, doi: 10.1016/j.jare.2021.03.015.
C. Tsallis, P. Papageorgas, D. Piromalis, and R. A. Munteanu, “Application-wise review of Machine Learning-based predictive maintenance: Trends, challenges, and future directions,” Applied Sciences, vol. 15, no. 9, pp. 4898, 2025, doi: 10.3390/app15094898.
S. Strbac Savic, J. Nedeljkovic Ostojic, Z. Gligoric, C. Cvijovic, and S. Aleksandrovic, “Operational Efficiency Forecasting Model of an Existing Underground Mine Using Grey System Theory and Stochastic Diffusion Processes,” Mathematical Problems in Engineering, vol. 2015, no. 1, Art. no. 610307, 2015, doi: 10.1155/2015/610307.
J. B. Awotunde, S. O. Folorunso, A. L. Imoize, J. O. Odunuga, C. C. Lee, C. T. Li, et al., “An ensemble tree-based model for intrusion detection in industrial internet of things networks,” Applied Sciences, vol. 13, no. 4, pp. 2479, 2023, doi: 10.3390/app13042479.
O. Lázaro, J. Alonso, P. Figueiras, R. Costa, D. Graça, G. Garcia, et al., “Big data-driven industry 4.0 service engineering large-scale trials: The boost 4.0 experience,” in Technologies and Applications for Big Data Value. Cham, Switzerland: Springer International Publishing, 2022, pp. 373-397, doi: 10.1007/978-3-030-78307-5_17.
T. Ji and X. Xu, “Exploring the integration of cloud manufacturing and cyber-physical systems in the era of Industry 4.0–An OPC UA approach,” Robotics and Computer-Integrated Manufacturing, vol. 93, Art. no. 102927, 2025, doi: 10.1016/j.rcim.2024.102927.
I. Varlamis, “Messy Data in Education: Enhancing Data Science Literacy Through Real-World Datasets in a Master’s Program,” Education Sciences, vol. 15, no. 4, pp. 500, 2025, doi: 10.3390/educsci15040500.