Automated Production Line Balancing Using Evolutionary Computing

  • Authors

    • David Wheeler Professor, University of Cambridge, United Kingdom. Author
    • Michael Gordon Professor of Computer Science, University of Cambridge, United Kingdom. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PII2V7Q

    Published 12-04-2024

  • Automated Production Line Balancing, Evolutionary Computing, Genetic Algorithm, Manufacturing Optimization, Smart Manufacturing, Industry 4.0, Assembly Line Optimization, Artificial Intelligence, Multi-objective Optimization, Production Scheduling

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    D. Wheeler and M. Gordon, “Automated Production Line Balancing Using Evolutionary Computing”, IJIARE, vol. 7, no. 2, pp. 01–18, Dec. 2024, doi: 10.67228/30715725/IJIARE-2024PII2V7Q.

  • Abstract

    Manufacturing industries are increasingly adopting Industry 4.0 technologies, including AI, IIoT, and cyber-physical systems, to enhance production efficiency and flexibility. Production line balancing remains a critical challenge due to dynamic production demands, resource constraints, and complex task dependencies. Traditional optimization methods often struggle with large-scale and multi-objective manufacturing environments. This paper proposes an Evolutionary Computing-based Automated Production Line Balancing Framework that employs adaptive genetic algorithms and multi-objective optimization to improve workstation assignment, workload balance, cycle time, throughput, and resource utilization. The framework supports dynamic manufacturing conditions through adaptive parameter tuning and intelligent constraint handling. Experimental evaluation demonstrates significant improvements in production efficiency, reduced idle time and bottlenecks, and enhanced operational flexibility, making the framework suitable for modern smart manufacturing systems.

  • References

    [1] S. Battaïa and A. Dolgui, "A taxonomy of line balancing problems and their solution approaches in smart manufacturing," IEEE Access, vol. 9, pp. 108214–108230, 2021.

    [2] H. Zhang, Y. Wang, and X. Li, "Multi-objective genetic algorithm for assembly line balancing in Industry 4.0 manufacturing systems," IEEE Access, vol. 9, pp. 131546–131559, 2021.

    [3] M. M. Hassan, M. R. Islam, and S. H. Shah, "Evolutionary optimization techniques for intelligent production scheduling: A comprehensive review," IEEE Access, vol. 10, pp. 25861–25882, 2022.

    [4] J. Wang, Z. Liu, and L. Chen, "Hybrid genetic algorithm and particle swarm optimization for production line balancing in smart factories," IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5478–5489, Aug. 2022.

    [5] Y. Zhao, H. Xu, and Q. Sun, "Adaptive evolutionary algorithms for dynamic manufacturing optimization under Industry 4.0," IEEE Access, vol. 10, pp. 90325–90342, 2022.

    [6] X. Chen, F. Tao, and A. Nee, "Digital twin-driven intelligent manufacturing: Recent advances and future perspectives," IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp. 2180–2194, Mar. 2023.

    [7] A. Kumar, P. Singh, and R. Sharma, "Multi-objective production line balancing using adaptive differential evolution," IEEE Access, vol. 11, pp. 32475–32491, 2023.

    [8] S. Li, T. Qiu, and Y. Zhang, "Industrial Internet of Things-enabled intelligent manufacturing optimization using evolutionary computing," IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12356–12369, Jul. 2023.

    [9] M. R. Khosravi and H. R. Karimi, "Artificial intelligence and evolutionary optimization for cyber-physical production systems," IEEE Transactions on Industrial Electronics, vol. 70, no. 11, pp. 11870–11882, Nov. 2023.

    [10] L. Gao, J. Li, and X. Wang, "Hybrid reinforcement learning and genetic algorithm for adaptive assembly line balancing," IEEE Access, vol. 12, pp. 18652–18668, 2024.

    [11] Y. Zhou, H. Guo, and F. Tao, "Digital twin-assisted intelligent scheduling for smart manufacturing: A survey," IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1087–1105, Apr. 2024.

    [12] M. Singh, A. Verma, and D. Gupta, "Edge computing-enabled real-time production optimization using evolutionary intelligence," IEEE Access, vol. 12, pp. 72811–72830, 2024.

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