University of Qom Journal of Engineering Management and Soft Computing 3116-0158 11 2 2025 12 01 Predictive maintenance in multi-objective supply chains by combining machine learning and evolutionary optimization 179 193 3865 10.22091/jemsc.2025.12514.1264 EN Koorosh Pouri PhD, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 0009-0008-6476-7418 Journal Article 2025 03 17 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. https://jemsc.qom.ac.ir/article_3865_ac7b6a2e4f433570a2b26df4e249171a.pdf