A Method for Analyzing Maintenance Decisions Based on the Discrete Markov Chain

• Autorzy: Kargar Shouroki Mohammad Hossein , Fallah Nezhad Mohammad Saber , Owlia Mohammad Saleh , Zare Hasan Khademi

Identyfikatory

DOI

10.24425/mper.2024.151487

• Języki publikacji: EN

Abstrakty

EN

Production systems are stopped due to malfunctions such as rotting equipment, imbalance of rotating parts, and high vibration, which leads to loss of customers, reduction of market share and unemployment of personnel. In this research, using the absorbing Markov process, a mathematical model is formulated to analyze the maintenance policy of the production process, through which one of the four states of new, old, or failure due to deterioration or sudden failure can be allocated to the machine. It is assumed that the machine changes from one state to another with different probabilities, which are determined using a discrete Markov chain. The different maintenance policies can be analyzed to minimize the average production cost. The mathematical model is obtained using discrete Markov chain equations, and the optimal maintenance and repair policy can be analyzed by considering all types of costs, including maintenance, production, and failure costs, so that the average cost of the production process can be minimized.

Słowa kluczowe

EN

absorbing Markov chains; cost minimization; maintenance; repairs; optimal decision

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2024
• Tom: Vol. 15, No. 3
• Strony: 1--7
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Kargar Shouroki Mohammad Hossein

• Department of Industrial Engineering, Yazd University, Yazd, Iran

autor

Fallah Nezhad Mohammad Saber

• Department of Industrial Engineering, Yazd University, P.O. BOX 89195- 741, Pejoohesh Street, Safa-ieh, Yazd, Iran

autor

Owlia Mohammad Saleh

• Department of Industrial Engineering, Yazd University, Yazd, Iran

autor

Zare Hasan Khademi

• Department of Industrial Engineering, Yazd University, Yazd, Iran

Bibliografia

• Alina, P., Frangu, L., Vilanova, R., & Caraman, S. (2020). A preventive maintenance strategy for an actuator using Markov models. IFAC PapersOnLine, 53 (2), 784–789.
• Al-jabouri, H., Saif, A., & Diallo, C. (2023). Robust selective maintenance optimization of series – parallel mission-critical systems subject to maintenance quality uncertainty. Computational Management Science, 20–29.
• Amari, S., McLaughlin, L., & Pham, H. (2006). Cost- Effective Condition-Based Maintenance Using Markov Decision Processes. Reliability and Maintainability Symposium.
• Andersson, F.J., Kulahci, M., & Nielsen, B. (2022). A numerical study of Markov decision process algorithms for multi-component replacement problems. European Journal of Operational Research, 299, 898–909.
• Angius, A., Colledani, M., Silipo, L., & Ymane, A. (2016). Impact of Preventive Maintenance on the Service Level of Multi-stage Manufacturing Systems with Degrading Machines. IFAC-PapersOnline, 49, 568–573.
• Dahia, Z., Bellaouar, A., & Dron, J. (2021). Maintenance Evaluation and Optimization of a Multi-State System Based on a Dynamic Bayesian Network. Management and Production Engineering Review, 12, 3–14.
• Dey, T., Samanta, G., & Sinha, S. (2023). CostOptimal Preventive Maintenance and Parts Replacement Schedule Using Mixed Integer Linear Programming. Journal of The Institution of Engineers (India): Series D, November.
• Fallah Nezhad, M.S. & Akhavan Niaki, S.T. (2010). Absorbing Markov Chain Models to Determine Optimum Process Target Levels in Production Systems with Rework and Scrapping. Journal of Industrial Engineering, 6, 1–6.
• Fang, L. & Zhaodong, H. (2015). System Dynamics Based Simulation Approach on Corrective Maintenance Cost of Aviation Equipments. Procedia Engineering, 99, 150–155.
• Gopalakrishnan, M., Skoogh, A., & Laroque, C. (2015). Planning of Maintenance Activities A Current State Mapping in Industry. Procedia CIRP, 30, 480–485.
• Hamrol, A. (2018). A new look at some aspects of maintenance and improvement of production processes. Management and Production Engineering Review, 9 (1), 34–43, DOI: 10.24425/119398
• Hongsheng, S.U., Xuping, D., & Dantong, W. (2021). Optimization of periodic maintenance for wind turbines based on stochastic degradation model. Archives of Electrical Engineering, 70 (3), 585–599.
• Jin, H., Han, F., & Sang, Y. (2020). An optimal maintenance strategy for multi-state deterioration systems based on a semi-Markov decision process coupled with simulation technique. Mechanical Systems and Signal Processing, 139, 1–22.
• Kumar, A., Sinwar, D., Kumar, N., & Saini, M. (2024). Performance optimization of generator in steam turbine power plants using computational intelligence techniques. Journal of Engineering Mathematics, 145 (12).
• Liu, Y. & Huang, H.-Z. (2010). Optimal replacement policy for multi-state system under imperfect maintenance. IEEE Transactions on Reliability, 59 (3), 483-495.
• Mahmud, I., Ismail, I., & Abdulkarim, A. (2024). Selection of an appropriate maintenance strategy using analytical hierarchy process of cement plant. Life Cycle Reliability and Safety Engineering, 13, 103–109.
• McCall, J. (1965). Maintenance Policies for Stochastically Failing Equipment: A Survey. Management Science, 11, 493–524.
• Quan, G., Greenwood, G., Liu, D., & Hu, S. (2007). Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms. European Journal of Operational Research, 177, 1969–1984.
• Rasay, H., Azizi, F., & Naderkhani, F. (2024). A mathematical maintenance model for a production system subject to deterioration according to a stochastic geometric process, Annals of Operations Research, 340, 451–478.
• Saini, M., Lal Patel, B., & Kumar, A. (2023). Stochastic Modelling and Performance Optimization of Marine Power Plant with Metaheuristic Algorithms. Journal of Marine Science and Application, 22, 751–761.
• Tomasevicz, C. & Asgarpoor, S. (2006). Optimum Maintenance Policy Using Semi-Markov Decision Processes. Electric Power Systems Research, 76, 452–456.
• Wan, S., Gao, J., Li, D., Tong, Y., & He, F. (2015). Web-based Process Planning for Machine Tool Maintenance and Services. Procedia CIRP, 38, 165–170.
• Yang, Y., Loxton, R., & Rohi, A. (2023). Long-term maintenance optimization for integrated mining operations, Optimization and Engineering, November 2023.