[1] R. van Dinter, B. Tekinerdogan, and C. Catal, “Predictive maintenance using digital twins: A systematic literature review,” Information and Software Technology, vol. 151, Art. no. 107008, 2022, doi: 10.1016/j.infsof.2022.107008.
[2] D. K. Yadav, A. Kaushik, and N. Yadav, “Predicting machine failures using machine learning and deep learning algorithms,” Sustainable Manufacturing and Service Economics, vol. 3, no. 1, pp. 1–10, 2024, doi: 10.1016/j.smse.2024.100029.
[3] R. Zimmermann and P. Brandtner, “From data to decisions: Optimizing supply chain management with machine learning-infused dashboards,” Procedia Computer Science, vol. 237, pp. 955–964, 2024, doi: 10.1016/j.procs.2024.05.184.
[4] A. Benhanifia, Z. Ben Cheikh, P. M. Oliveira, A. Valente, and J. Lima, “Systematic review of predictive maintenance practices in the manufacturing sector,” Intelligent Systems with Applications, vol. 26, Art. no. 200501, 2025, doi: 10.1016/j.iswa.2025.200501.
[5] M. M. Ershadi, M. Momeni Sharifabad, M. J. Ershadi, A. Azizi, and S. Behzadipour, “Multi-objective modeling of green vehicle routing problem using a hybrid extreme learning machine and genetic programming,” Supply Chain Management, vol. 25, no. 81, pp. 17–41, 2024 (in Persian).
[6] S. Abedi, M. R. Karimi, and A. Alinezhad, “Predicting and analyzing pathways toward sustainable development of the mining sector under the NZE scenario,” Journal of Mining and Environment, in press, 2025, doi: 10.22044/jme.2025.16846.3304.
[7] A. Boujarif, D. W. Coit, O. Jouini, Z. Zeng, and R. Heidsieck, “Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency,” Reliability Engineering & System Safety, vol. 255, Art. no. 110642, 2025, doi: 10.1016/j.ress.2024.110642.
[8] Bahramzad,M , Abedi,S and Ehtesham Rasi,R . (2027). Designing a Predictive Model for Product Acceptance Rate in Lean Manufacturing Using an Artificial Neural Network. System Engineering and Productivity, 6(4), 55-80. doi: 10.22034/sep.2026.2079391.1442
[9] U. Dereci and G. Tuzkaya, “An explainable artificial intelligence model for predictive maintenance and spare parts optimization,” Supply Chain Analytics, vol. 8, Art. no. 100078, 2024, doi: 10.1016/j.sca.2024.100078.
[10] Bahramzad,M , Abedi,S and Ehtesham Rasi,R . (2026). A data-driven approach to extracting rules governing product acceptance rates in a lean production environment: A case study of Zar Food Industries. (e733999). Modern Research in Decision Making, (), e733999
[11] N. Elkhenin and H. Mrad, “Maintenance 4.0 in mining trucks: Data digitalization and advanced protocols,” Procedia Computer Science, vol. 253, pp. 2147–2155, 2025, doi: 10.1016/j.procs.2025.01.275.
[12] M. Gallab, H. Bouloiz, E. Garbolino, M. Tkiouat, M. A. ElKilani, and N. Bureau, “Risk analysis of maintenance activities in a LPG supply chain with a multi-agent approach,” Journal of Loss Prevention in the Process Industries, vol. 47, pp. 41–56, 2017, doi: 10.1016/j.jlp.2017.02.026.
[13] J. Li, J. Milisavljevic-Syed, and K. Salonitis, “Predictive maintenance servitisation pathways,” IFAC-PapersOnLine, vol. 58, no. 8, pp. 329–334, 2024, doi: 10.1016/j.ifacol.2024.08.142.
[14] H. Lin, J. Lin, and F. Wang, “An innovative machine learning model for supply chain management,” Journal of Innovation & Knowledge, vol. 7, no. 4, Art. no. 100276, 2022, doi: 10.1016/j.jik.2022.100276.
[15] Y.-Y. Liu, K.-H. Chang, and Y.-Y. Chen, “Simultaneous predictive maintenance and inventory policy in a continuously monitoring system using simulation optimization,” Computers & Operations Research, vol. 153, Art. no. 106146, 2023, doi: 10.1016/j.cor.2023.106146.
[16] P. Mallioris, E. Aivazidou, and D. Bechtsis, “Predictive maintenance in Industry 4.0: A systematic multi-sector mapping,” CIRP Journal of Manufacturing Science and Technology, vol. 50, pp. 80–103, 2024, doi: 10.1016/j.cirpj.2024.02.003.
[17] A. Murtaza, A. Saher, M. H. Zafar, S. K. R. Moosavi, M. F. Aftab, and F. Sanfilippo, “Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0,” Results in Engineering, vol. 24, Art. no. 102935, 2024, doi: 10.1016/j.rineng.2024.102935.
[18] P. Nunes, J. Santos, and E. Rocha, “Challenges in predictive maintenance – A review,” CIRP Journal of Manufacturing Science and Technology, vol. 40, pp. 53–67, 2023, doi: 10.1016/j.cirpj.2022.11.004.
[19] B. Shaheen, Á. Kocsis, and I. Németh, “Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks,” Engineering Applications of Artificial Intelligence, vol. 119, Art. no. 105749, 2023, doi: 10.1016/j.engappai.2022.105749.
[20] A. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliability Engineering & System Safety, vol. 215, Art. no. 107864, 2021, doi: 10.1016/j.ress.2021.107864.
[21] E. Traini, G. Bruno, G. D’Antonio, and F. Lombardi, “Machine learning framework for predictive maintenance in milling,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 177–182, 2019, doi: 10.1016/j.ifacol.2019.11.172.
[22] Y.-C. Tsao, A. Pantisoontorn, T.-L. Vu, and T.-H. Chen, “Optimal production and predictive maintenance decisions for deteriorated products under advance-cash-credit payments,” International Journal of Production Economics, vol. 269, Art. no. 109132, 2024, doi: 10.1016/j.ijpe.2023.109132.
[23] S. Ahmadi and R. Yousefpour, “A hybrid data mining and data-driven supply chain modeling algorithm for warehouse allocation and warehouse-to-customer service,” Journal of Industrial Management Perspective, vol. 11, no. 3, pp. 269–295, 2021 (in Persian).
[24] S. Asadi Shomia, S. Nosratpanah, and M. M. Mohtadi, “Assessing organizational readiness for implementing an online computerized maintenance management system,” Andisheh Amad Journal, vol. 20, no. 76, pp. 1–34, 2021 (in Persian).
[25] M. Esmaeili, L. Alfat, M. Amiri, and I. Raeisi Vanani, “Classification and allocation of suppliers to customers in a resilient supply chain using machine learning approaches,” Journal of Industrial Management Perspective, vol. 13, no. 3, pp. 39–70, 2023 (in Persian).
[26] M. Esmaili and Z. Goodarzi, “Application of game theory approach in selected maintenance problems,” Journal of Industrial Engineering, vol. 52, no. 3, pp. 309–324, 2018 (in Persian).
[27] F. Afsharnia and A. Marzban, “Investigating the long-term effect of preventive maintenance strategy on operational efficiency and failure rate trend using time series analysis,” Journal of Agricultural Machinery, vol. 10, no. 2, pp. 347–359, 2020 (in Persian).
[28] M. Pourhabib Yekta Sharmi and R. Shafaei, “Optimal solution of supply chain management problem in thermal power plants,” International Journal of Industrial Engineering and Production Management, vol. 28, no. 3, pp. 447–458, 2017 (in Persian).
[29] R. Taghipour and S. Avakh Dastani, “Selecting an appropriate maintenance strategy using a fuzzy hierarchical approach,” Industrial Management Studies, vol. 16, no. 50, pp. 193–228, 2018 (in Persian).
[30] M. Touhidi and H. Fayyazi, “Presenting aircraft maintenance strategies based on total productive maintenance approach,” Interdisciplinary Studies of Strategic Knowledge, vol. 14, no. 57, pp. 147–171, 2024 (in Persian).
[31] M. M. Mohtadi, “The conceptualization of sustainable resistive maintenance as an indigenous maintenance strategy (research action: a military organization),” Supply Chain Management, vol. 23, no. 72, pp. 71–82, 2022, doi: 20.1001.1.20089198.1400.23.72.6.2 (in Persian).
[32] M. Mohammadi, S. Khalili, S. Ramezani, and M. Mosadegh-Khah, “Presentation of a decision-making model for long-term storage (conservation) of defense items (case study: armor equipments),” Supply Chain Management, vol. 26, no. 85, pp. 89–105, 2025, doi: 20.1001.1.20089198.1403.26.85.6.9 (in P
ersian).
