Abstract
In this paper, a new approach to maintenance scheduling for a multi-component production system which takes into account the real-time information from workstations including remaining reliability of equipments as well as work-in-process inventories in each workstation is proposed. To model dynamics of the system, other information like production line configuration, cycle times, buffers’ capacity and mean time to repair of machines are also considered. Using factorial experiment design the problem is formulated to comprehensively monitor the effects of each possible schedule on throughput of the production system. The optimal maintenance schedule is searched by genetic algorithm-based optimization engine implemented in a simulation optimization platform. The proposed approach exploits all of makespans of planning horizon to find the best opportunity to perform maintenance actions on degrading machines in a way that maximizes the system throughput and mitigates the production losses caused by imperfect traditional maintenance strategies. Finally the proposed method is tested in a real production line to magnify the accuracy of proposed scheduling method. The experimental results indicate that the proposed approach guarantees the operational productivity and scheduling efficiency as well.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Budai G., Huisman D., Dekker R. (2006) Scheduling preventive railway maintenance activities. Journal of the Operational Research Society 57: 1035–1044
Chang Q., Ni J., Bandyopadhyay P., Biller S., Xiao G. (2007) Maintenance opportunity planning system. Journal of Manufacturing Science and Engineering 129: 661–668
Dagpunar J. S. (1996) A Maintenance model with opportunities and interrupt replacement options. Journal of Operations Research Society 47(11): 1406–1409
Dietl C., Rakowsky U. K. (2006) An operating strategy for high-availability multi-station transfer lines. International Journal of Automation and Computing 3(2): 125–130
Fu M. C. (2002) Optimization for simulation: Theory vs. practice. Journal on Computing 14: 192–215
Fu, M., Glover, F., & April, J. (2005). Simulation optimization: A review, new developments and applications. In Proceedings of 37th winter simulation conference, Orlando (pp. 83–95).
Grigoriev A., Klundert J., Spieksma F. (2006) Modeling and solving the periodic maintenance problem. European Journal of Operational Research 172: 783–797
Harrell, C., Ghosh, B., & Bowden, R. (2004). Simulation using ProModel. McGraw-Hill.
Higgins A. (1998) Scheduling of railway track maintenance activities and crews. Journal of the Operational Research Society 49: 1026–1033
Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: Michigan Press.
Kampen A. H. C., Buydens L. M. C., Strom C. S. (1996) Lethalization, penalty and repair function for constraint handling in the genetic algorithm methodology. Chemommetrics and Intelligent Laboratory Systems 34(1): 55–68
Langdon W., Treleaven P. (1997) Scheduling maintenance of electrical power transmission networks using genetic programming. In: Warwick K., Ekwue A., Aggarwal R. (eds) Artificial intelligence techniques in power systems. Institution of Electrical Engineers, Stevenage
Montgomery D. C., Runger G. C. (2006) Applied statistics and probability for engineers. Wiley, New York
Ni J., Djurdjanovic J., Qiu H., Liao H. (2006) Intelligent prognostics tools and e-maintenance. Journal of Computers in Industry 52: 476–489
Nicolai, R. P., & Dekker, R. (2008). Optimal maintenance of multi-component systems: A review. In K. A. H. Kobbacy & D. N. P. Murthy (Eds.), Complex system maintenance handbook (pp. 263–286). Springer.
Ólafsson, S. (2006). Metaheuristics. In S. G. Henderson & B. L. Nelson (Eds.), Handbook in operations research and management science (pp. 633–654). Elsevier(2006) In: (eds) Wiley, New York
Papadakis I., Kleindorfer P. (2005) Optimizing infrastructure network maintenance when benefits are interdependent. OR Spectrum 27: 63–84
Rakowsky U. K. (2006) Modelling, reliability-adaptive multisystem operation. International Journal of Automation and Computing 3(2): 192–198
Rogers, P. (2005). Optimum-seeking simulation in the design and control of manufacturing systems experience with OptQuest for Arena. In Proceedings of 37th winter simulation conference, Orlando (pp. 1142–1150).
Schutz J., Rezg N., Léger J. (2009) Periodic and sequential preventive maintenance policies over a finite planning horizon with a dynamic failure law. Journal of Intelligent Manufacturing 22(4): 523–532
Sheu S. H., Jhang J. P. (1996) A generalized group maintenance policy. European Journal of Operational Research 96: 232–247
Wang H. (2002) A survey of maintenance policies of deteriorating systems. European Journal of Operational Research 139: 469–489
Yang Z., Chang Q., Djurdjanovic D., Ni J. (2007a) Maintenance scheduling for a manufacturing system of machines with adjustable throughput. IIE Transactions 39(12): 1111–1125
Yang Z., Chang Q., Djurdjanovic D., Ni J., Lee J. (2007b) Maintenance priority assignment utilizing on-line production information. Journal of Manufacturing Science and Engineering 129: 435–446
Yang Z., Djurdjanovic D., Ni J. (2008) Maintenance scheduling in manufacturing systems based on predicted machine degradation. Journal of Intelligent Manufacturing 19(1): 87–98
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arab, A., Ismail, N. & Lee, L.S. Maintenance scheduling incorporating dynamics of production system and real-time information from workstations. J Intell Manuf 24, 695–705 (2013). https://doi.org/10.1007/s10845-011-0616-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-011-0616-3