Presenting an Integrated Model for Production Planning and Preventive Maintenance Scheduling Considering Uncertainty of Parameters and Disruption of Facilities

Document Type : Original Article


1 Assistant Professor of Industrial Management, Faculty of Management, Electronic Unit, Islamic Azad University, Tehran, Iran.

2 Assistant Professor of Information Technology Management, Faculty of Management, Electronic Unit, Islamic Azad University, Tehran, Iran.

3 Assistant Professor of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

4 Ph.D, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.


The scheduling of parallel machines and preventive maintenance is one of the key issues in the field of production processes, and has always been a topic of interest for researchers. This research aims to design an integrated model for scheduling and preventive maintenance for parallel machines considering the probability of disruption in facilities and uncertainty in parameters of the model. In this regard, a mathematical scheduling model has been proposed with two objective functions of minimizing the weighted completion time of products and maximizing the reliability of the production line. The NP-hard nature of the studied problem from a computational perspective, meta-heuristic algorithms such as NSGA-II and MOPSO were utilized to solve numerical problems in medium and large scales. Therefore, numerical problems were designed in different size and solved by the proposed algorithms. The results showed that the NSGA-II compared to the MOPSO algorithm provide better solutions. However, MOPSO has better efficiency than NSGA-II in term of computation time, this superiority is not considerable and it can not be considered as a definitive basis for comparing two algorithms.


Main Subjects

  1. Abbassi, R., Arzaghi, E., Yazdi, M., Aryai, V., Garaniya, V., & Rahnamayiezekavat, P. (2022). Risk-based and predictive maintenance planning of engineering infrastructure: existing quantitative techniques and future directions. Process Safety and Environmental Protection. Volume 165, Pages 776-790.
  2. Aghaee, M., & Fazli, S. (2012). Applying a Hybrid DEMATEL and ANP Approach for Suitable Maintenance Approach Selection (Case Study: Work Vehicle Industry). The Journal of Industrial Manajement Perspective, 6, 89-107. (In Persian)
  3. Amiri, S., & Honarvar, M. (2018). Providing an integrated Model for Planning and Scheduling Energy Hubs and preventive maintenance. Energy, 163, 1093-1114.
  4. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolutionary Computing, Indian, 6(2), 182-197.
  5. Ertem, M., As' ad, R., Awad, M., & Al-Bar, A. (2022). Workers-constrained shutdown maintenance scheduling with skills flexibility: Models and solution algorithms. Computers & Industrial Engineering, 172, 108575.
  6. Fitouhi, M. C., & Nourelfath, M. (2014). Integrating noncyclical preventive maintenance scheduling and production planning for multi-state systems. Reliability Engineering & System Safety, 121, 175-186.
  7. Ghalami, L., & Grosu, D. (2019). Scheduling parallel identical machines to minimize makespan: A parallel approximation algorithm. Journal of Parallel and Distributed Computing, 133, 221-231.
  8. Jafar-Zanjani, H., Zandieh, M., & Sharifi, M. (2022). Robust and resilient joint periodic maintenance planning and scheduling in a multi-factory network under uncertainty: A case study. Reliability Engineering & System Safety, 217, 108113.
  9. Jiménez, M., Arenas, M., Bilbao, A., & Rodríguez, M.V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
  10. Liu, X., Wang, W., & Peng, R. (2017). An integrated preventive maintenance and production planning model with sequence‚Äźdependent setup costs and times. Quality and Reliability Engineering International, 33(8), 2451-2461.
  11. Miyata, H. H., Nagano, M. S., & Gupta, J. N. (2019). Integrating preventive maintenance activities to the no-wait flow shop scheduling problem with dependent-sequence setup times and makespan minimization. Computers & Industrial Engineering, 135, 79-104.
  12. Nadizadeh Ardakani, A., Ranjbar, H., & Moubed, M. (2020). Periodic Inspection Optimization for a TwoComponent System with Dependent Failures. The Journal of Industrial Manajement Perspective, 38, 83-110. (In Persian)
  13. Nattaf, M., Dauzère-Pérès, S., Yugma, C., & Wu, C. H. (2019). Parallel machine scheduling with time constraints on machine qualifications. Computers & Operations Research, 107, 61-76.
  14. Ruiz R., & Maroto C. (2016). A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility. European Journal of Operational Research, 169(3), 781-800.
  15. Safarzadeh, H., & Akhavan Niaki, S. T. (2019). Bi-objective green scheduling in uniform parallel machine environments. Journal of Cleaner Production, 217, 559-572.
  16. Sajedinejad, A., & Lotfi, M. (2019). Providing a model to optimize preventive maintenance schedules for multi-component systems using GA. Industrial Management Studies, 55, 137-160. (In Persian)
  17. Shaukat, S., Katscher, M., Wu, C.L., Delgado, F., Larrain, H. (2020) Aircraft line maintenance scheduling and optimization. Journal of Air Transport Management, 89,
  18. Shayanian, M., & Behnamian, J. (2017). Discrete Particle Swarm Optimization for Job Shop Scheduling Problem with Parallel Machine. International Journal of Industrial Engineering & Production Management, 28(1), 15-26. (In persian)
  19. Shen, L., Dauzère-Pérès, S., & Neufeld, J. S. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research, 265(2), 503-516.
  20. Sherafat, A., Mohaghar, A., Karimi, F., & Davoodi, S.M.R. (2018). Designing the Mechanism for Choosing the Appropriate Maintenance Strategy. The Journal of Industrial Manajement Perspective, 6, 31-69. (In Persian)
  21. Soper, A. J., & Strusevich, V. A. (2019). Schedules with a single preemption on uniform parallel machines. Discrete Applied Mathematics, 261, 332-343.
  22. Wang, T., Baldacci, R., Lim, A., & Hu, Q. (2018). A branch-and-price algorithm for scheduling of deteriorating jobs and flexible periodic maintenance on a single machine. European Journal of Operational Research, 271(3), 826-838.
  23. Wang, S., & Ye, B. (2019). Exact methods for order acceptance and scheduling on unrelated parallel machines. Computers & Operations Research, 104, 159-173.
  24. Yang, L., Ma, X., Peng, R., Zhai, Q., & Zhao, Y. (2017). A preventive maintenance policy based on dependent two-stage deterioration and external shocks. Reliability Engineering & System Safety, 160, 201-211.
  25. Zhong, S., Pantelous, A. A., Goh, M., & Zhou, J. (2019). A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms. Mechanical Systems and Signal Processing, 124, 643-663.