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

Document Type : Original Article

Author

PhD, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

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.

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Belhor, M., El-Amraoui, A., Jemai, A., & Delmotte, F. (2023). Multi-objective evolutionary approach based on K-means clustering for home health care routing and scheduling problem. Expert Systems with Applications, 213, 119035.‏ https://doi.org/10.1016/j.eswa.2022.119035
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28. https://doi.org/10.1007/s10668-022-02350-2
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.‏ https://doi.org/10.1109/4235.996017
Delshad, M. M., Chobar, A. P., Ghasemi, P., & Jafari, D. (2024). Efficient Humanitarian Logistics: Multi-Commodity Location–Inventory Model Incorporating Demand Probability and Consumption Coefficients. Logistics, 8(1), 9. https://doi.org/10.3390/logistics8010009
Fathi Hafshejani, K., Bagheri Sorkhi, M., & Modiri, M. (2023). Integrated hybrid model of sustainable supply chain in cement industry. Engineering Management and Soft Computing, 9(1), 1-18. doi: 10.22091/JEMSC.2021.6422.1144
Jiang, Y., Dai, P., Fang, P., Zhong, R. Y., Zhao, X., & Cao, X. (2022). A2-LSTM for predictive maintenance of industrial equipment based on machine learning. Computers & Industrial Engineering, 172, 108560.‏ https://doi.org/10.1016/j.cie.2022.108560
Nguyen, T. H., & Jung, J. J. (2021). Swarm intelligence-based green optimization framework for sustainable transportation. Sustainable Cities and Society, 71, 102947.‏ https://doi.org/10.1016/j.scs.2021.102947
Niavand, M., Adibi, M. A., & Pourghader Chobar, A. (2024). Selection of green supplier by multi-moora combination method and two-stage clustering. Engineering Management and Soft Computing, 10(1), 14-49. doi: 10.22091/jemsc.2024.10977.1181
sazegari, S., davoodi, S. M., & goli, A. (2024). Designing a green supply chain pricing model with a multi-criteria decision-making approach and game theory (case study: home appliance industry). Engineering Management and Soft Computing, 10(1), 92-122. doi: 10.22091/jemsc.2024.11144.1191
Sharma, D. K., Brahmachari, S., Singhal, K., & Gupta, D. (2022). Data driven predictive maintenance applications for industrial systems with temporal convolutional networks. Computers & Industrial Engineering, 169, 108213.‏ https://doi.org/10.1016/j.cie.2022.108213
Yadav, D. K., Kaushik, A., & Yadav, N. (2024). Predicting machine failures using machine learning and deep learning algorithms. Sustainable Manufacturing and Service Economics, 3, 100029.‏ https://doi.org/10.1016/j.smse.2024.100029
Hosseini, S., Rezaeenour, J., Masoumi, M., & Akbari, A. H. (2021). A The Evaluation of Knowledge Management in Supply Chain Using EFQM Framework, Fuzzy Multi-Attribute Decision Making and Multi-Objective Programming. Industrial Management Studies, 19(60), 193-235.
Rezaenoor, J., Saadi, G., & Akbari, A. (2019). Design of a Decision Support System to Diagnose and Predict Heart Disease using Artificial Neural Network; a case study (Ayatollah Golpayegani Hospital in Qom). Management Strategies in Health System, 3(4), 320-331.
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