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Maintenance is an essential activity in every manufacturing establishment, as manufacturing effectiveness counts on the functionality of production equipment and machinery in terms of their productivity and operational life. Maintenance cost minimization can be achieved by adopting an appropriate maintenance planning policy. This paper applies the Markovian approach to maintenance planning decision, thereby generating optimal maintenance policy from the identified alternatives over a specified period of time. Markov chains, transition matrices, decision processes, and dynamic programming models were formulated for the decision problem related to maintenance operations of a cable production company. Preventive and corrective maintenance data based on workloads and costs, were collected from the company and utilized in this study. The result showed variability in the choice of optimal maintenance policy that was adopted in the case study. Post optimality analysis of the process buttressed the claim. The proposed approach is promising for solving the maintenance scheduling decision problems of the company.



Maintenance policy Preventive Corrective Workload Markov-chains

Article Details

How to Cite
Kareem, B., & Owolabi, H. (2012). Optimizing Maintenance Planning in the Production Industry Using the Markovian Approach. The Journal of Engineering Research [TJER], 9(2), 46–63.


  1. Aas K, Eikvil L, Huseby RB (1999), Applications of hidden Markov chains in image analysis. Pattern Recognition 32(4):703-713.
  2. Akanbi OG, Oluleye AE, Onanuga MA (2001), Inventory model for deteriorating items with inflationary factors. Nigerian Journal of Engineering Management 2(1):28-34.
  3. Ansell A, Racutanu G, Sunquist H (2001), A Markov approach in estimating the service life of bridge elements in Sweden. Proceedings of the 9th International Conference on the Durability of Building Materials and Components, Brisbane, CSIRO BCE, Paper no.142.
  4. Anthony TF, Taylor BW (1977), Analyzing the predictive capabilities of Markovian analysis of air pollution level variations. Journal of Environmental Management 2:139-149.
  5. Azadivar F, Shu JV (1998), Use of simulation in optimization of maintenance policies. Proceedings of Winter Simulation Conference 1061-1066.
  6. Bayraktar E, Ludkovski M (2009), Sequential tracking of a hidden Markov chain using point process observations. Stochastic Processes and their Applications 119(6):1792-1822
  7. Corotis RB, Ellis JH, Jiang M (2005), Modelling of risked-based inspection, maintenance and lifecycle cost with partially observable Markov decision process. Structure and Infrastructure Engineering 1(1):75-84.
  8. Debussche M, Gordon M, Lepart J, Romane F (1977) An account of the use of a transition matrix. Agro-Ecosystems 3:81-92.
  9. Duong T, Phung D, Bui H, Venkatesh S (2009), Efficient duration and hierarchical modeling for human activity recognition. Artificial Intelligence 173(7-8):830-856.
  10. Elliott RJ, Siu TK (2009), Robust optimal portfolio choice under Markovian regime-switching model. Methodology and Computing in Applied Probability 11(2 SPEC. ISS.):145-157.
  11. Forbes AF, Batholomew DJ (1979), Statistical Techniques for Manpower Planning. John Wiley, New York.
  12. Inegbenebor AO, Adeniji FA (2002) Impact of Maintenance on the Productivity of some companies in the North-Eastern States of Nigeria. Nigerian Journal of Industrial and Systems Studies 1(1):19-27.
  13. Ming N, Peter B, Xiong B, Chang S (2004), An inventory control policy for maintenance networks. Proceedings of IEEE/RS International Conference on Intelligent Robots and Systems 1238-1243.
  14. Ozgur-Unluakin D, Bilgic T (2006), Predictive maintenance using dynamic probabilistic networks. PGM Workshop 45(2):215-232.
  15. Rabinar RL (1989), A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE l(77)257-280.
  16. Sheik AK, Raout A, Sekerday UA, Younas M (1989), Optimal tool replacement and resetting strategies in automated manufacturing systems. International Journal of Production Research 37(4):917-937.
  17. Taha HA (2008), Operations research: An introduction. Prentice-Hall of India, New Delhi.
  18. Tsao HY, Lin PC, Pitt L, Campbell C (2009), The impact of loyalty and promotion effects on retention rate. J. of the Operational Research Society 60(5):646-651.
  19. Usher MB (1979), Markovian approaches to ecological success. J. of Animal Ecology 48:413-426.
  20. Vandeveer LR, Drummond HE (1978), The use of Markov processes in estimating land use change. Tech Bull. No.148. Oklahoma; Agricultural Experimental Station.