A Review of Production Line Balancing and Optimization for Manufacturing Enterprises in the Context of Industry 4.0

Jiayu Wei; Yao He

doi:10.54691/v52y9h32

Authors

Jiayu Wei School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China
Yao He School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China

DOI:

https://doi.org/10.54691/v52y9h32

Keywords:

Industry 4.0; Production Line Balancing; Intelligent Manufacturing; Optimization Algorithms; Digital Twin; Strategy and Tactics.

Abstract

As global manufacturing moves toward intelligent, digital transformation, the concept of Industry 4.0 has created an enormous demand for highly flexible and responsive production systems. To meet this demand, manufacturing enterprises of all kinds are actively adopting advanced technologies such as the Internet of Things, digital twins, and artificial intelligence to optimize production processes and reduce resource waste. This paper provides a comprehensive and systematic review of the current state of research on production line balancing and its optimization strategies in the context of Industry 4.0, in order to fill the conceptual gap left by the unclear relationship between traditional line balancing theory and the dynamic requirements of modern intelligent manufacturing. The findings show that academic attention has gradually shifted from traditional mathematical programming and static heuristic algorithms toward dynamic planning theories driven by real-time data, human-machine collaboration, and cyber-physical systems. The intelligent manufacturing technologies that enterprises adopt at the strategic and tactical levels profoundly shape line balance and efficiency through distinct elements and mechanisms. This review helps managers and production engineers identify best practices in the field and thus provides effective guidance for building smart factories and making operational decisions.

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References

[1] Zhang, M., & Qi, M. (2025). Research on the four dimensional new type organizational element matching model for the digital transformation of traditional manufacturing enterprises. Technology and Innovation Management, 46(5), 493–508. https://doi.org/10.14090/j.cnki.jscx.2025.0501.

[2] Yang, L. (2026). Lean automated production improvement based on production line balancing. In Jiangxi Society of Automotive Engineering (Ed.), Proceedings of the Academic Symposium on Engineering Technology Innovation and Development (Volume I) (pp. 1486–1489). https://doi.org/10.26914/c.cnkihy.2026.011612

[3] Zhou, W., & Ding, C. (2025). Evaluation of the German government’s Industry 4.0 financing policy framework and its implications for China’s intelligent manufacturing transformation. Shenzhen Social Sciences, 8(5), 104–115. https://doi.org/10.20183/j.cnki.szshkx.20250826.002

[4] Cheng, J. (2025, May 16). Key technologies and applications of intelligent manufacturing in the context of Industry 4.0. Anhui Science and Technology News, p. 016. https://doi.org/10.27992/n.cnki.nahkj.2025.000249

[5] Jiang, F., & Luo, Y. (2026, March 9). Research on the application effectiveness of digital twin technology in intelligent manufacturing workshops. Shanxi Science and Technology News, p. A06.

[6] Guo, Z., & Pang, R. (2025). Research on data analysis and optimization models for production line balancing improvement. China Plant Engineering, (13), 138–140.

[7] Yuan, P., Liu, J., Liu, C., & et al. (2022). Research on multi objective optimization methods for the mixed model assembly line balancing problem. Operations Research and Management Science, 31(10), 26–32.

[8] Li, J., & Wang, W. (2024). A solution model for the multi objective assembly line balancing problem based on a hybrid optimization algorithm. China New Technologies and Products, (1), 142–145. https://doi.org/10.13612/j.cnki.cntp.2024.01.044

[9] Bai, Y. (2026). Research on balancing optimization of the final assembly line in an aircraft manufacturing company. Journal of Physics: Conference Series, 3221(1), 012065. https://doi.org/10.1088/1742 6596/3221/1/012065

[10] Liao, Y. (2024). Research on UAV inspection path optimization based on the simulated annealing algorithm. Power and Energy, 45(3), 320–323.

[11] Guerrero, R., Serrano Hernandez, A., Pascual, J., & et al. (2022). Simulation model for wire harness design in the car production line optimization using the SimPy Library. Sustainability, 14(12), 7212. https://doi.org/10.3390/su14127212

[12] Yao, X., Zhang, J., Tao, T., & et al. (2018). Transformation and upgrading of the manufacturing industry from lean agile manufacturing to Industry 4.0 long tail production. Computer Integrated Manufacturing Systems, 24(10), 2377–2387. https://doi.org/10.13196/j.cims.2018.10.001

[13] Jing, X. (2025). Research on innovation strategies for the digital transformation of industrial sectors in the context of Industry 4.0. Modern Industrial Economy and Informationization, 15(9), 89–91. https://doi.org/10.16525/j.cnki.14 1362/n.2025.09.029

[14] Wang, H., Zhu, Z., & Feng, J. (2025). Research on the construction of smart factories in the context of Industry 4.0. Industrial Control Computer, 38(11), 157–159.

[15] Ren, H., Fan, X., & Yu, D. (2026). Research on intelligent production line optimization based on the Industry 4.0 framework. Modern Industrial Economy and Informationization, 16(4), 79–81. https://doi.org/10.16525/j.cnki.14 1362/n.2026.04.023

[16] Deng, Y. (2026). Innovation in reliability inspection and testing technology for electronic components oriented toward Industry 4.0. China Brand and Anti counterfeiting, (6), 78–80.