An optimal maintenance policy for machine replacement problem using dynamic programming

Abstract: In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by consid-ering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling including renew, repair and do nothing and wish to achieve an optimal threshold for making decisions including renew, repair and continue the production in order to minimize the expected cost. Results show that the optimal policy is sensitive to the data, for the probability of defective machines and parameters defined in the model. This can be clearly demonstrated by a sensitivity analysis technique.

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 Amalnik, M & Pourgharibshahi, M. (2017). An optimal maintenance policy for machine replacement problem using dynamic programming.Management Science Letters , 7(6), 311-320.

Refrences

De Almeida, A. T., & Bohoris, G. A. (1995). Decision theory in maintenance decision making. Journal of Quality in Maintenance Engineering, 1(1), 39-45.
Amari, S. V., McLaughlin, L., & Pham, H. (2006, January). Cost-effective condition-based maintenance using Markov decision processes. In Reliability and Maintainability Symposium, 2006. RAMS'06. Annual (pp. 464-469). IEEE.
Burhanuddin, M. A., Ahmad, A. R., & Desa, M. I. (2007, May). An application of decision making grid to improve maintenance strategies in small and medium industries. In Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on (pp. 455-460). IEEE.
Fernandez, O., Labib, A. W., Walmsley, R., & Petty, D. J. (2003). A decision support maintenance management system: development and implementation. International Journal of Quality & Reliability Management, 20(8), 965-979.
Gürler, Ü., & Kaya, A. (2002). A maintenance policy for a system with multi-state components: an approximate solution. Reliability Engineering & System Safety, 76(2), 117-127.
Gupta, S., Maiti, J., Kumar, R., & Kumar, U. (2009). A control chart guided maintenance policy selection. International Journal of Mining, Reclamation and Environment, 23(3), 216-226.
El-Gohary, A. (2004). Estimations of parameters in a three state reliability semi-Markov model. Applied mathematics and computation, 154(2), 389-403.
Ivy, J. S., & Nembhard, H. B. (2005). A modeling approach to maintenance decisions using statistical quality control and optimization. Quality and Reliability Engineering International, 21(4), 355-366.
Khalil, J., Saad, S. M., Gindy, N., & MacKechnie, K. (2005). A maintenance policy selection tool for industrial machine parts. In Emerging Solutions for Future Manufacturing Systems (pp. 431-440). Springer US.
Kuo, Y. (2006). Optimal adaptive control policy for joint machine maintenance and product quality control. European Journal of Operational Research, 171(2), 586-597.
Labib, A. W. (1998). World-class maintenance using a computerised maintenance management system. Journal of Quality in Maintenance Engineering, 4(1), 66-75.
Labib, A. W. (2004). A decision analysis model for maintenance policy selection using a CMMS. Journal of Quality in Maintenance Engineering, 10(3), 191-202.
Lugtigheid, D., & Jardine, A. K. (2004). Modelling repairable system reliability with explanatory variables and repair and maintenance actions. IMA Journal of Management Mathematics, 15(2), 89-110.
Lu, K. Y., & Sy, C. C. (2009). A real-time decision-making of maintenance using fuzzy agent. Expert Systems with Applications, 36(2), 2691-2698.
Madu, C. N. (2000). Competing through maintenance strategies. International Journal of Quality & Reliability Management, 17(9), 937-949.
Martorell, S., Sanchez, A., & Serradell, V. (1999). Age-dependent reliability model considering effects of maintenance and working conditions. Reliability Engineering & System Safety, 64(1), 19-31.
Ming Tan, C., & Raghavan, N. (2007). Root cause analysis based maintenance policy. International Journal of Quality & Reliability Management, 24(2), 203-228.
Mobley, R. K. (2002). An introduction to predictive maintenance. Butterworth-Heinemann.
Montgomery, D. C. (2007). Introduction to statistical quality control. John Wiley & Sons.
Nezhad, M. S. F., Momeni, M., Sayani, N. N., & Akhoondi, F. (2015). Optimal sequential sampling plans using dynamic programming approach. Pakistan Journal of Statistics and Operation Re-search, 11(4).
Samrout, M., Châtelet, E., Kouta, R., & Chebbo, N. (2009). Optimization of maintenance policy using the proportional hazard model. Reliability Engineering & System Safety, 94(1), 44-52.
Swanson, L. (2001). Linking maintenance strategies to performance. International Journal of Production Economics, 70(3), 237-244.
Tersine, R. J. (1985). Production/operations management: concepts, structure, and analysis. New York: North-Holland.
Tahir, Z., Burhanuddin, M. A., Ahmad, A. R., Halawani, S. M., & Arif, F. (2009, December). Improvement of decision making grid model for maintenance management in small and medium industries. In Industrial and Information Systems (ICIIS), 2009 International Conference on (pp. 598-603). IEEE.
Xia, T., Xi, L., Lee, J., & Zhou, X. (2011). Optimal CBPM policy considering maintenance effects and environmental condition. The International Journal of Advanced Manufacturing Technology, 56(9-12), 1181-1193.

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