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  11. Harmonized Integration Of Gwo And J-slno For Optimized Asset Management And Predictive Maintenance In Industry 4.0

Harmonized Integration of GWO and J-SLnO for Optimized Asset Management and Predictive Maintenance in Industry 4.0

A N Arularasan1, P Ganeshkumar2, Mohammad Alkhatib2

  • 1Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai 600 048, Tamil Nadu, India.

Sensors (Basel, Switzerland)
|May 14, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Advanced optimization algorithms, Grey Wolf Optimization (GWO) and Jaya-based Sea Lion Optimization (J-SLnO), significantly improve Industry 4.0 asset management and predictive maintenance efficiency and cost-effectiveness.

Area of Science:

  • Industrial Engineering
  • Computer Science
  • Operations Research

Background:

  • Industry 4.0 necessitates advanced solutions for asset management and predictive maintenance.
  • Traditional scheduling algorithms (MinMin, MaxMin, FCFS, Round Robin) often fall short in efficiency and cost-effectiveness.
  • Optimization algorithms offer potential for enhanced industrial processes.

Purpose of the Study:

  • To apply and evaluate Grey Wolf Optimization (GWO) and Jaya-based Sea Lion Optimization (J-SLnO) for asset management and predictive maintenance in Industry 4.0.
  • To compare the performance of GWO and J-SLnO against traditional scheduling algorithms.
  • To assess the impact of these algorithms on efficiency, cost, and energy consumption.

Main Methods:

  • Implementation of Grey Wolf Optimization (GWO) and Jaya-based Sea Lion Optimization (J-SLnO).
Keywords:
Grey Wolf Optimization (GWO)Industry 4.0J-SLnOoptimization algorithms

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  • Integration of these algorithms with resource scheduling techniques.
  • Comparative analysis against standard algorithms: MinMin, MaxMin, FCFS, and Round Robin.

    • Main Results:

    • GWO reduced execution time by 13-31%; J-SLnO by 16-33%.
    • GWO offered cost savings of 8.57-9.17%; J-SLnO achieved 13.56-19.71% savings.
    • Both algorithms demonstrated significant energy efficiency, reducing consumption by 94.1-94.2%.
    • J-SLnO showed superior accuracy and stability in predictive modeling.

    Conclusions:

    • GWO and J-SLnO provide substantial improvements in efficiency, cost-effectiveness, and energy consumption for Industry 4.0 asset management.
    • J-SLnO is particularly effective for accurate and reliable predictive maintenance applications.
    • The study validates these advanced optimization techniques for practical industrial implementation, paving the way for smarter operations.
    predictive maintenance