Determining Maintenance Opportunity Window (MOW) in Job-Shop Systems by Considering Manpower of Maintenance

Document Type : Research Paper

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Nowadays, production systems seek to integrate production and maintenance activities. An effective maintenance plan can improve maintenance stability and system performance. Machines that stop for repairing operation impose a high cost on the system. On the other hand, there are always some intangible situations during a production process in which repairing activities can be carried out. If they are detected, system productivity can be improved. The main purpose of this study is specifying Maintenance Opportunity Window (MOW) in job-shop production systems. For this purpose, mathematical models and formulae were developed in order to determine the MOW in a way that they could provide maximum repairing time for the machine and, as a result, the lowest disturbance occurring in production. This model also determines the number of lost products during PM. Considering the manpower of maintenance and M/M/1//k queueing model, the terms required for repairs are addressed. Finally, numerical experiments on and sensitivity analysis of critical parameters of the model, such as the initial level of the buffers and processing rates of the machines, are considered. Model validation is carried out by comparison of the results with a simulation model. In this study, some suggestions for improving the system are proposed.

Keywords


Barlow, R., & Hunter, L. (1960). Optimum preventive maintenance policies. Operations Research, 8(1), 90–100.
Buzacott, J. A., & Shanthikumar, J. G. (1993). Stochastic models of manufacturing systems (Vol. 4). Prentice Hall Englewood Cliffs, NJ.
Chang, Q., Ni, J., Bandyopadhyay, P., Biller, S., & Xiao, G. (2007). Maintenance opportunity planning system. Journal of Manufacturing Science and Engineering, 129(3), 661–668.
Coolen-Schrijner, P., Shaw, S. C., & Coolen, F. P. (2009). Opportunity-based age replacement with a one-cycle criterion. Journal of the Operational Research Society, 60(10), 1428–1438.
Cui, L., & Li, H. (2006). Opportunistic maintenance for multi-component shock models. Mathematical Methods of Operations Research, 63(3), 493–511.
Doyen, L., & Gaudoin, O. (2011). Modeling and assessment of aging and efficiency of corrective and planned preventive maintenance. IEEE Transactions on Reliability, 60(4), 759–769.
Gershwin, S. B. (1994). Manufacturing systems engineering. Prentice Hall.
Gu, X., Jin, X., Guo, W., & Ni, J. (2017). Estimation of active maintenance opportunity windows in Bernoulli production lines. Journal of Manufacturing Systems, 45, 109–120.
Gu, X., Jin, X., & Ni, J. (2015). Prediction of passive maintenance opportunity windows on bottleneck machines in complex manufacturing systems. Journal of Manufacturing Science and Engineering, 137(3), 031017.
Gu, X., Lee, S., Liang, X., Garcellano, M., Diederichs, M., & Ni, J. (2013). Hidden maintenance opportunities in discrete and complex production lines. Expert Systems with Applications, 40(11), 4353–4361.
Gu, X., Lee, S., Liang, X., & Ni, J. (2012). Extension of maintenance opportunity windows to general manufacturing systems. In Proc. of ASME 2012 International Manufacturing Science and Engineering Conference, Nortre Dame, IN.(MSEC2012-7346).
Guo, W., Jin, J. J., & Hu, S. J. (2013). Allocation of maintenance resources in mixed model assembly systems. Journal of Manufacturing Systems, 32(3), 473–479.
Keith, M. R. (2002). An introduction to predictive maintenance. Butterworth-Heinemann.
Keizer, M. C. O., Teunter, R. H., Veldman, J., & Babai, M. Z. (2018). Condition-based maintenance for systems with economic dependence and load sharing. International Journal of Production Economics, 195, 319–327.
Koochaki, J., Bokhorst, J. A., Wortmann, H., & Klingenberg, W. (2012). Condition based maintenance in the context of opportunistic maintenance. International Journal of Production Research, 50(23), 6918–6929.
Lee, S., Gu, X., Garcellano, M., Diederichs, M., & Ni, J. (2013). Discovery of hidden maintenance opportunities in automotive assembly lines: MOW and GMOW. The International Journal of Advanced Manufacturing Technology, 68(9–12), 2611–2623.
Lee, S., Gu, X., & Ni, J. (2013). Stochastic maintenance opportunity windows for unreliable two-machine one-buffer system. Expert Systems with Applications, 40(13), 5385–5394.
Li, J., Blumenfeld, D. E., Huang, N., & Alden, J. M. (2009). Throughput analysis of production systems: recent advances and future topics. International Journal of Production Research, 47(14), 3823–3851.
Li, J., & Meerkov, S. M. (2008). Production systems engineering. Springer Science & Business Media.
Li, Y., Chang, Q., Jin, X., & Ni, J. (2015). Stochastic energy opportunity windows in advanced manufacturing systems. ASME Paper No. MSEC2015-9257.
Liu, B., Wu, S., Xie, M., & Kuo, W. (2017). A condition-based maintenance policy for degrading systems with age-and state-dependent operating cost. European Journal of Operational Research.
Ni, J., Gu, X., & Jin, X. (2015). Preventive maintenance opportunities for large production systems. CIRP Annals-Manufacturing Technology, 64(1), 447–450.
Ni, J., & Jin, X. (2012). Decision support systems for effective maintenance operations. CIRP Annals-Manufacturing Technology, 61(1), 411–414.
Sheu, S. H., Lin, Y. B., & Liao, G. L. (2006). Optimum policies for a system with general imperfect maintenance. Reliability Engineering & System Safety, 91(3), 362–369.
Yang, Z., Chang, Q., Djurdjanovic, D., Ni, J., & Lee, J. (2007). Maintenance priority assignment utilizing on-line production information. Journal of Manufacturing Science and Engineering, 129(2), 435–446.
Zou, J., Chang, Q., Lei, Y., Xiao, G., & Arinez, J. (2015). Stochastic Maintenance Opportunity Windows for Serial Production Line. In ASME 2015 International Manufacturing Science and Engineering Conference (p. V002T04A007–V002T04A007). American Society of Mechanical Engineers.