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

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