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

AN Arularasan and P Ganeshkumar and M Alkhatib and T Albalawi, SENSORS, 25, 2896 (2025).

DOI: 10.3390/s25092896

Highlights What are the main findings? The study shows that, in terms of execution speed, cost-effectiveness, and energy usage, GWO and J-SLnO perform better than conventional scheduling algorithms. Compared to GWO, J-SLnO performs predictive maintenance activities with greater accuracy and stability. What is the implication of the main finding? In Industry 4.0, the suggested optimization strategies improve the effectiveness of predictive maintenance and asset management. J-SLnO is a dependable option for practical industrial applications that demand endurance and excellent prediction accuracy.Highlights What are the main findings? The study shows that, in terms of execution speed, cost-effectiveness, and energy usage, GWO and J-SLnO perform better than conventional scheduling algorithms. Compared to GWO, J-SLnO performs predictive maintenance activities with greater accuracy and stability. What is the implication of the main finding? In Industry 4.0, the suggested optimization strategies improve the effectiveness of predictive maintenance and asset management. J-SLnO is a dependable option for practical industrial applications that demand endurance and excellent prediction accuracy.Abstract The study encompasses the application of two different advanced optimization algorithms on asset management and predictive maintenance in Industry 4.0-Grey Wolf Optimization and Jaya-based Sea Lion Optimization (J-SLnO). Using this derivative, the authors showed how these techniques could be combined through resource scheduling techniques to demonstrate drastic improvement in the level of efficiency, cost-effectiveness, and energy consumption, as opposed to the standard MinMin, MaxMin, FCFS, and Round Robin. In this sense, GWO results in an execution time reduction between 13 and 31%, whereas, in J-SLnO, there is an execution time reduction of 16-33%. In terms of cost, GWO shows an advantage of 8.57-9.17% over MaxMin and Round Robin, based on costs, while J-SLnO delivers a better economy for the range of savings achieved, which is between 13.56 and 19.71%. Both algorithms demonstrated tremendous energy efficiency, according to the analysis, which showed 94.1-94.2% less consumption of energy than traditional methods. Moreover, J-SLnO was reported to be more accurate and stable in predictability, making it an excellent choice for accurate and more time-trusted applications. J-SLnO is being increasingly recognized as a powerful yet realistic solution for the application of Industry 4.0 because of efficacy and reliability in predictive modeling. Not only does this research validate these optimization techniques to better use in practical life, but it also extends recommendations for putting the techniques into practice in industrial settings, thus laying the foundation for smarter, more efficient asset management and maintenance processes.

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