Document Type : Original Article


1 Ph.D Student, Department of Industrial Engineering, Science and Research Unit, Islamic Azad University, Tehran, Iran.

2 Associate Professor, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran.

3 Assistant Professor, Department of Industrial Engineering, Science and Research Unit, Islamic Azad University, Tehran, Iran.


In this paper, we study a production system that is subject to network failures and produces perishable goods. We assume that the system has preventive and corrective maintenance activities and can return defective items for rework. Our objective is to find the optimal production rate that minimizes the total cost of production, inventory, spoilage, and maintenance over a long planning horizon. We consider the uncertainty of machine failures and use discrete event simulation and ARENA.14 software to estimate the performance measures of the system. We also use data envelopment analysis to evaluate the efficiency of the system and identify the best scenario. The results show the effectiveness of our proposed model.


Main Subjects

  1. Ahmadi, S, H., & Grossi Mokhtarzadeh, N. (2014). Investigating and prioritizing the level of sensitivity of devices for repairs and preventive maintenance with the Martel and Zaras model (case study: Fire Production Machinery Company). Industrial Management, 2(5), 1-22. (In Persian)
  2. Afshar-Bakeshloo, M., Bozorgi-Amiri, A., Sajadi, S. M., & Jolai, F. (2018). A multi-objective Environmental Hedging Point Policy with customer satisfaction criteria. Journal of Cleaner Production, 179, 478-494.
  3. Afzali, P., Keynia, F., & Rashidinejad, M. (2019). A new model for reliability-centered maintenance prioritisation of distribution feeders. Energy, 171, 701-709.
  4. Amelian, S., Sajadi, S. M., & Alinaghian, M. (2015). Optimal production and preventive maintenance rate in a failure-prone manufacturing system using discrete event simulation. International Journal of Industrial and Systems Engineering, 20(4), 483-496.
  5. Amelian, S. S., Sajadi, S. M., Navabakhsh, M., & Esmaelian, M. (2019). Multi-objective optimization of stochastic failure-prone manufacturing system with consideration of energy consumption and job sequences. International journal of environmental science and technology, 16(7), 3389-3402.
  6. Amelian, S. S., Sajadi, S. M., Navabakhsh, M., & Esmaelian, M. (2022). Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method.Expert Systems, 39(2),
  7. Behnamfar, R., Sajadi, S. M., & Tootoonchy, M. (2022). Developing environmental hedging point policy with variable demand: A machine learning approach. International Journal of Production Economics, 254,
  8. Caballé, N., Castro, I., Pérez, C. & Lanza-Gutiérrez, J. M. (2015). A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety, 134, 98-109.
  9. Charnes, A., W. Cooper, & Rhodes, E., (1987). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2, 429–444.
  10. Chopra, A. (2021). Applications and barriers of reliability centered maintenance (RCM) in various industries: a review. Industrial Engineering Journal, 14(1), 15-24
  11. Davari, A., Ganji, M., & Sajadi, S. M. (2022). An integrated simulation-fuzzy model for preventive maintenance optimisation in multi-product production firms. Journal of Simulation, 16(4), 374-391.
  12. Deiranlou, M., Azadjou, F. & Sajjadi, S.M. (1401). Presenting the simulation-optimization model of production systems prone to network failure with the approach of maintenance and repairs based on reliability and revenue sharing. The Journal of Industrial management perspective 12(4), 131-158 (In Persian).
  13. Dhyne, E., Petrin, A., Smeets, V., & Warzynski, F. (2020). Theory for extending single-product production function estimation to multi-product settings. Work. Pap., Yale Univ., New Haven, CT Google Scholar Article Location.
  14. Eslami, S., Sajadi, S. M., & Kashan, A. H. (2014). Selecting a preventive maintenance scheduling method by using simulation and multi criteria decision making. International Journal of Logistics Systems and Management, 18(2), 250-269.
  15. Fatahi, A., Sajadi, S. M., Yazdian, S. A. (1401). Simulation-based optimization in multi-product three-level production systems with multi-purpose machines (case study: pipes and fittings, single-wall and double-wall). Sharif industrial engineering and management, 28(1), 37-49 (in Persian).
  16. Gao, Z., Wang, H., & Zhang, H. (2022). The Decision of Production Systems with Quality-Contingent Demand and Condition-Based Maintenance. Systems, 10(1),
  17. Haj Shirmohammadi, A. (1385). Principles of planning and control of production and inventories. Isfahan, Arkan Danesh publications (In Persian).
  18. Hatami-Marbini, A., Sajadi, S. M., & Malekpour, H. (2020). Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods. Computers & Industrial Engineering, 146,
  19. Heydari Dahoui, J., Sajjadi, S.M. & Tavan, F. (2021). Designing the processes of small and medium businesses in the field of perishable goods in order to design an optimal production policy with a simulation approach. Management research in Iran, 19(3), 7-35 (In Persian).
  20. Kelton, D., Sadoski, R. & Stark, D. (2013). Translated by Bagheri, M., Ali Sabouye, A. & Hossein Hejazi, T. Simulation with ERNA software. Ferdowsi University of Mashhad Press, first edition (In Persian).
  21. Kheradranjbar, M., Mohammadi, M., & Rafiee, S. (2022). Evaluating the Efficiency of Building Repair and Maintenance System Using Data Envelopment Analysis Method. Journal of Structural and Construction Engineering,8(Special Issue 4), 252-269.
  22. Mahluji, H. (2008). Simulation of Discrete Systems – Contingency. Scientific Publications of Sharif University of Technology, Tehran (In Persian).
  23. Malekpour, H., Sajadi, S. M., & Vahdani, H. (2016). Using discrete-event simulation and the Taguchi method for optimising the production rate of network failure-prone manufacturing systems with perishable goods. International Journal of Services and Operations Management, 23(4), 387-406.
  24. Mehrgan, M. (1391). Quantitative models in evaluating the performance of organizations. Tehran, Academic Book Publishing (in Persian).
  25. Mousavi, S., Sajjadi, S.M., Tabriz, A. & Najafi, S.I. (1400). Hierarchical network design of urban temporary treatment centers in crisis conditions with the integrated approach of mathematical model-simulation. The Journal of Industrial Management Perspectives 11(2), 12-99(in Persian).
  26. Naylor, T.H., & Finger, J.M. (1981). Verification of Computer Simulation Studies. Management Science, 24, 180-189.
  27. Pratap, S., Daultani, Y., Dwivedi, A., & Zhou, F. (2021). Supplier selection and evaluation in e-commerce enterprises: a data envelopment analysis approach. Benchmarking: An International Journal, 29(1), 325-341.
  28. Polotski, V., Kenne, J. P., & Gharbi, A. (2019). Joint production and maintenance optimization in flexible hybrid Manufacturing–Remanufacturing systems under age-dependent deterioration. International Journal of Production Economics, 216, 239-254.
  29. Sajjadi, S.M. (1389). Determining the production rate in production systems prone to network failure with fixed demand rate, PhD thesis, Faculty of Industrial Engineering, Amirkabir University of Technology (In Persian).
  30. Sajadi, S. M., Alizadeh, A., Zandieh, M., & Tavan, F. (2019). Robust and stable flexible job shop scheduling with random machine breakdowns: multi-objectives genetic algorithm approach. International journal of mathematics in operational research, 14(2), 268-289.
  31. Sajadi, S.M. Seyed Esfahani, M.M. S¨ rensen, K. (2011). Production control in a failure-prone manufacturing network using discrete event simulation and automated response surface methodology. Int J Adv Manuf Technol, 53(1-4), 35–46.
  32. Shamayleh, A., Awad, M., & Abdulla, A. O. (2019). Criticality-based reliability-centered maintenance for healthcare. Journal of Quality in Maintenance Engineering.
  33. Shannon R.E. (1975). System Simulation: The Art and Science. Prentice-Hall.
  34. Swaan Arons, D., Attila Boer, C., Storage and retrieval of discrete - event simulation. Simulation Practice and Theory, 8(8), 555-576, 2001.
  35. Tavan, F., & Sajadi, S. M. (2015). Determination of optimum of production rate of network failure prone manufacturing systems with perishable items using discrete event simulation and Taguchi design of experiment. Journal of Industrial Engineering and Management Studies, 2(1), 16-26.
  36. Van Jaarsveld, W., Dekker, R., (2011). Spare parts stock control for redundant systems using reliability centered maintenance data. Reliab Eng Syst Saf, 96, 1576–86.
  37. Vishnu, C. R., & Regikumar, V. (2016). Reliability Based Maintenance Strategy Selection in Process Plants: A Case Study. Procedia Technology, 25, 1080-1087.
  38. Wong, W. P. (2021). A Global Search Method for Inputs and Outputs in Data Envelopment Analysis: Procedures and Managerial Perspectives. Symmetry, 13(7),
  39. Xie, X. (1989). Optimal control in a failure prone manufacturing system”, Automatic Control, IEEE, 31, 116-126.
  40. Zhu, J. (Ed.). (2016). Data envelopment analysis: A handbook of empirical studies and applications (Vol. 238). New York: Springer.