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Pouri, Koorosh. (1404). Predictive maintenance in multi-objective supply chains by combining machine learning and evolutionary optimization. پژوهش‌های فلسفی -کلامی, 11(2), 179-193. doi: 10.22091/jemsc.2025.12514.1264
Koorosh Pouri. "Predictive maintenance in multi-objective supply chains by combining machine learning and evolutionary optimization". پژوهش‌های فلسفی -کلامی, 11, 2, 1404, 179-193. doi: 10.22091/jemsc.2025.12514.1264
Pouri, Koorosh. (1404). 'Predictive maintenance in multi-objective supply chains by combining machine learning and evolutionary optimization', پژوهش‌های فلسفی -کلامی, 11(2), pp. 179-193. doi: 10.22091/jemsc.2025.12514.1264
Pouri, Koorosh. Predictive maintenance in multi-objective supply chains by combining machine learning and evolutionary optimization. پژوهش‌های فلسفی -کلامی, 1404; 11(2): 179-193. doi: 10.22091/jemsc.2025.12514.1264

Predictive maintenance in multi-objective supply chains by combining machine learning and evolutionary optimization

Journal of Engineering Management and Soft Computing
مقاله 8، دوره 11، شماره 2 - شماره پیاپی 21، اسفند 2025، صفحه 179-193    اصل مقاله (856.15 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22091/jemsc.2025.12514.1264
نویسنده
Koorosh Pouri*
PhD, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده
Predictive maintenance, as a new approach to industrial equipment management, uses machine learning to predict failure probability and evolutionary optimization algorithms to determine optimal maintenance strategies. In this study, a hybrid model is presented that first estimates the failure probability of equipment using XGBoost and LSTM algorithms and then uses NSGA-II and PSO to optimize maintenance decisions. The results show that the NSGA-II algorithm performs better than PSO in reducing maintenance costs by 42.8% and reducing equipment downtime by 55.4%. The innovation of this research lies in integrating machine learning and evolutionary optimization into an intelligent and efficient framework that can reduce operating costs, increase equipment reliability, and optimize maintenance strategies. The findings of this research can be used in various industries to improve productivity and reduce unexpected failures.
کلیدواژه‌ها
Predictive Maintenance؛ Machine Learning؛ Evolutionary Optimization؛ NSGA-II؛ PSO
مراجع

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