Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization


Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases: firstly, this study predicts required capacity of each machine through extreme learning machine algorithm. Based on analyzing historical data, the opportunistic periods are calculated for implementing PM tasks to have less impact on production and obtain the PM interval and duration. Secondly, this study obtains the scheduling planning and the least number of maintenance personnel through an improved ant colony optimization algorithm. Finally, the feasibility and benefits of this approach are investigated based on empirical study by using historical data from real manufacturing execution system and equipment maintenance system. Experimental results demonstrate the effectiveness of proposed approach, reduce personnel number while guarantee the maintenance tasks. Therefore, the proposed approach is beneficial to improve the company’s production efficiency.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17


\(\beta \) :

weight matrix of ELM output layer

B :

bias matrix of ELM hidden layer

C :

the adjusted parameter of ELM

\(g(\cdot )\) :

activation function

I :

identity matrix

L :

number of ELM hidden layer nodes

\(L_{lop}\) :

last overlap list

\(L_{op}\) :

overlap list

m :

number of ELM input features

N :

number of ELM training samples

n :

number of ELM output caregories

\(N_p\) :

the number of PM tasks

\(N_{ant}\) :

the number of ants in ACO algorithm

\(S_d\) :

total distance

T :

output matrix of ELM

\(T_c\) :

sum of \(T_{prc_i}, 1\le i\le 12\) in the past for each machine

\(T_{cc}\) :

CM cycle time

\(T_{cds_i}\) :

hourly CM duration of day shift

\(T_{cds}\) :

CM duration of day shift

\(T_{cd}\) :

CM duration of machine

\(T_{maxc}\) :

maximum sum of \(T_{prc_i}, 1\le i\le 12\) in the past for each machine

\(T_{maxpd}\) :

maximum PM duration

\(T_{minc}\) :

minimum sum of \(T_{prc_i}, 1\le i\le 12\) in the past for each machine

\(T_{minpd}\) :

minimum PM duration

\(T_{op_i}\) :

hourly opportunity time

\(T_{p_i}\) :

PM duration of one PM type

\(T_{pc}\) :

PM cycle time

\(T_{prc_i}\) :

hourly predicted required capacity

\(Tb_d\) :

distance table

\(Tb_p\) :

path table

X :

input feature matrix of ELM training samples

W :

weight matrix of ELM hidden layer


ant colony optimization


corrective maintenance


extreme learning machine


equipment maintenance system


manufacturing execution system


mean square error


principal component analysis


predictive maintenance


preventative maintenance


traveling salesman problem


  1. Ab-Samat, H., & Kamaruddin, S. (2014). Opportunistic maintenance (OM) as a new advancement in maintenance approaches: A review. Journal of Quality in Maintenance Engineering, 20(2), 98–121.

    Article  Google Scholar 

  2. Aizpurua, J. I., Catterson, V. M., Papadopoulos, Y., Chiacchio, F., & D’Urso, D. (2017). Supporting group maintenance through prognostics-enhanced dynamic dependability prediction. Reliability Engineering & System Safety, 168, 171–188.

    Article  Google Scholar 

  3. Barlow, R., & Hunter, L. (1960). Optimum preventive maintenance policies. Operations research, 8(1), 90–100.

    Article  Google Scholar 

  4. Berenguer, C., Chu, C., & Grall, A. (1997). Inspection and maintenance planning: An application of semi-Markov decision processes. Journal of intelligent manufacturing, 8(5), 467–476.

    Article  Google Scholar 

  5. Boland, P. J. (1982). Periodic replacement when minimal repair costs vary with time. Naval Research Logistics Quarterly, 29(4), 541–546.

    Article  Google Scholar 

  6. Butollo, F. & Lüthje, B. (2017). ‘Made in China 2025’: Intelligent manufacturing and work. The new digital workplace. How new technologies revolutionise work, pp. 42–61.

  7. Canfield, R. V. (1986). Cost optimization of periodic preventive maintenance. IEEE Transactions on Reliability, 35(1), 78–81.

    Article  Google Scholar 

  8. Chalabi, N., Dahane, M., Beldjilali, B., & Neki, A. (2016). Optimisation of preventive maintenance grouping strategy for multicomponent series systems: Particle swarm based approach. Computers and Industrial Engineering, 102, 440–451.

    Article  Google Scholar 

  9. Colorni, A., Dorigo, M., Maniezzo, V., et al. (1992). Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life (Vol. 142, pp. 134–142). Cambridge, MA.

  10. Van Do, P., Barros, A., Bérenguer, C., Bouvard, K., & Brissaud, F. (2013). Dynamic grouping maintenance with time limited opportunities. Reliability Engineering and System Safety, 120, 51–59.

    Article  Google Scholar 

  11. Fitouhi, M.-C., & Nourelfath, M. (2012). Integrating noncyclical preventive maintenance scheduling and production planning for a single machine. International Journal of Production Economics, 136(2), 344–351.

    Article  Google Scholar 

  12. Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics), 42(2), 513–529.

    Article  Google Scholar 

  13. Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1–3), 489–501.

    Article  Google Scholar 

  14. Jezzini, A., Ayache, M., Elkhansa, L., Makki, B., & Zein, M. (2013). Effects of predictive maintenance (PdM), Proactive maintenace (PoM) and Preventive maintenance (PM) on minimizing the faults in medical instruments. In: 2013 2nd International conference on advances in biomedical engineering (pp. 53–56). IEEE.

  15. Ji, M., He, Y., & Cheng, T. E. (2007). Single-machine scheduling with periodic maintenance to minimize makespan. Computers and operations research, 34(6), 1764–1770.

    Article  Google Scholar 

  16. Keizer, M. C. O., Flapper, S. D. P., & Teunter, R. H. (2017). Conditionbased maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research, 261(2), 405–420.

    Article  Google Scholar 

  17. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business and Information Systems Engineering, 6(4), 239–242.

    Article  Google Scholar 

  18. Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.

    Article  Google Scholar 

  19. Li, L. (2018). China’s manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”. Technological Forecasting and Social Change, 135, 66–74.

    Article  Google Scholar 

  20. Lin, K.-Y. (2018). User experience-based product design for smart production to empower industry 4.0 in the glass recycling circular economy. Computers and Industrial Engineering, 125, 729–738.

    Article  Google Scholar 

  21. Lin, Z.-L., Huang, Y.-S., & Fang, C.-C. (2015). Non-periodic preventive maintenance with reliability thresholds for complex repairable systems. Reliability Engineering and System Safety, 136, 145–156.

    Article  Google Scholar 

  22. Nakagawa, T. (1986). Periodic and sequential preventive maintenance policies. Journal of Applied Probability, 23(2), 536–542.

    Article  Google Scholar 

  23. Pereira, C. M., Lapa, C. M., Mol, A. C., & Da Luz, A. F. (2010). A particle swarm optimization (pso) approach for non-periodic preventive maintenance scheduling programming. Progress in Nuclear Energy, 52(8), 710–714.

    Article  Google Scholar 

  24. Reinelt, G. (1991). TSPLIB’A traveling salesman problem library. ORSA Journal on Computing, 3(4), 376–384.

    Article  Google Scholar 

  25. Saaksvuori, A., & Immonen, A. (2008). Product lifecycle management. Berlin: Springer.

    Book  Google Scholar 

  26. Sheikhalishahi, M., Azadeh, A., & Pintelon, L. (2017). Dynamic maintenance planning approach by considering grouping strategy and human factors. Process Safety and Environmental Protection, 107, 289–298.

    Article  Google Scholar 

  27. Stark, J. (2015). Product lifecycle management. In: Product lifecycle management (Vol. 1, pp. 1–29). Springer, Cham.

  28. Valdez, F., Moreno, F., & Melin, P. (2020). A Comparison of ACO, GA and SA for Solving the TSP Problem. In: Hybrid intelligent systems in control, pattern recognition and medicine (pp. 181–189). Springer, Cham.

  29. van Lier, B. (2014). Developing the industrial internet of things with a network centric approach: A holistic scientific perspective on smart industries. In: 2014 18th International conference on system theory, control and computing (ICSTCC) (pp. 324–329). IEEE.

  30. Venkataraman, V. (2007). Maintenance engineering and management. New Delhi: PHI Learning Pvt. Ltd.

    Google Scholar 

  31. Vu, H. C., Do, P., Barros, A., & Berenguer, C. (2014). Maintenance grouping strategy for multi-component systems with dynamic contexts. Reliability Engineering and System Safety, 132, 233–249.

    Article  Google Scholar 

  32. Wübbeke, J., Meissner, M., Zenglein, M. J., Ives, J., & Conrad, B. (2016). Made in China 2025: The making of a high-tech superpower and consequences for industrial countries. Mercator Institute for China Studies, 17, 2017–09.

    Google Scholar 

  33. Xia, T., Jin, X., Xi, L., & Ni, J. (2015). Production-driven opportunistic maintenance for batch production based on MAM-APB scheduling. European Journal of Operational Research, 240(3), 781–790.

    Article  Google Scholar 

  34. Yak, Y., Forward, K., & Dillon, T. (1985). Modelling the effect of transient faults in fault tolerant computer systems. IFAC Proceedings Volumes, 18(12), 129–133.

    Article  Google Scholar 

  35. Zhou, X., & Shi, K. (2019). Capacity failure rate based opportunistic maintenance modeling for series-parallel multi-station manufacturing systems. Reliability Engineering and System Safety, 181, 46–53.

    Article  Google Scholar 

Download references


This research was supported by National Key R&D Program of China, No. 2018YFE0105000, the National Natural Science Foundation of China under Grant No. 51475334, the Shanghai Municipal Commission of science and technology No. 19511132100 and the Fundamental Research Funds for the Central Universities of China under Grant No. 22120170077.

Author information



Corresponding author

Correspondence to Kuo-Yi Lin.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, L., Wang, Y. & Lin, KY. Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization. J Intell Manuf 32, 545–558 (2021).

Download citation


  • Complex manufacturing system
  • Preventive maintenance
  • Production-maintenance synchronization
  • Extreme learning machine
  • Ant colony optimization