Maintenance policy selection of N-component repairable system using genetic algorithm

  • Nishit Kumar Srivastava ICFAI Business School, Hyderabad
  • Pratyay Kuila
  • Namrata Chatterjee
  • A K Subramani
  • N Akbar Jan
Keywords: Artificial Intelligence (AI), corrective maintenance, preventive maintenance, redictive maintenance, Genetic Algorithm (GA)


A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n- component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.


Aarab, S., Barkany, A.E., & Khalfi, A.E. (2017). The integration of maintenance plans and production scheduling taking account of outsourcing: a literature review, International Journal of Productivity and Quality Management, 21 (1), 1–22.
Abbas, N.N., Ahmed, T., Shah, S.H.U., Omar, M., & Woo, H. (2019). Investigating the applications of artificial intelligence in cyber security. Scientometrics, 121 (2), 1189-1211.
Afif, M.H., Ghareb, A.S., Saif, A., Bakar, A.A., & Bazighifan, O. (2020). Genetic algorithm rule-based categorization method for textual data mining. Decision Science Letters, 9 (1),37–50.
Ahmad, R., & Kamaruddin S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63 (1), 135-149.
Arjestan, M.E. (2017). Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems. Decision Science Letters, 6, 307-322.
Azadeh, A., & Zadeh, S.A. (2015). An integrated analytic hierarchy process and fuzzy multiple- criteria-decision-making simulation approach for maintenance policy selection. Simulation, 92 (1), 3-18.
Babu, A.S., Sanghi, U., Rehman, A.U., Shayea, A.M.A., & Sharaf, M.A.F. (2013). A model to derive software maintenance policy decision. International Journal of Productivity and Quality Management, 11 (3), 247–268.
Bevilacqua M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering and System Safety, 70 (1), 71–83.
Costa, C.A.B., Carnero, M.C., & Oliveira, M.D. (2012). A multi-criteria model for auditing a predictive maintenance programme. European Journal of Operational Research, 217 (2), 381– 393.
Curcuru, G., Galante, G., & Lombardo, A. (2010). A predictive maintenance policy with imperfect monitoring. Reliability Engineering and System Safety, 95 (9), 989–997.
da Silva, P.R.N., Negrao, M.M.L.C., Vieira, P.J. & Sanz-Bobi, M.A. (2012). A new methodology of fault location for predictive maintenance of transmission lines. Electrical Power and Energy Systems, 42 (1), 568–574.
Darwish, M.A., Alenezi, A.R., & Goyal, S.K. (2016). Determination of maintenance schedule and process mean of production system. International Journal of Productivity and Quality Management, 17 (2), 258–271.
Dhillon, B.S. (2002). Engineering maintenance- A modern approach. CRC Press. Florida.
Engelbrecht, A.P. (2002). Computational Intelligence: An Introduction. John Wiley & Sons Inc., Hoboken, NJ 07030, USA.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, United States.
Guo, B., Song, S., Ghalambor, A., & Lin, T.R. (2014) Offshore Pipelines, 2nd ed. Elsevier, Waltham, USA.
Haupt, R.L., & Haupt, S.E. (2004). Practical Genetic Algorithms. John Wiley & Sons, Inc., Hoboken, New Jersey.
Hsiao, Y.L., Drury, C., Wu, C., & Paquet, V. (2013). Predictive models of safety based on audit findings: Part 1: Model development and reliability. Applied Ergonomics, 44 (2), 261-273.
Hu, J., Zhang, L., & Liang, W. (2012). Opportunistic predictive maintenance for complex multi-component systems based on DBN-HAZOP model. Process Safety and Environmental Protection, 90 (5), 376–388.
Ierace, S., & Cavalieri, S. (2008). Maintenance strategy selection: a comparison between fuzzy logic and analytic hierarchy process. 9th IFAC Workshop on Intelligent Manufacturing Systems, Szczecin. Poland. 228-233.
Joo, S.J., & Min, H. (2013). A multiple objective approach to scheduling the preventive maintenance of modular aircraft components. International Journal of Services and Operations Management, 9 (1), 18 – 31.
Kinnear, K.E. (1994). Advances in Genetic Programming. MIT Press. Cambridge, MA 02142, US.
Kuila, P., Gupta, S.K., & Jana. PK. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48- 56.
Limmun, W., Borkowski, J. J., & Chomtee, B. (2013). Using a Genetic Algorithm to Generate D-optimal Designs for Mixture Experiments. Quality and Reliability Engineering International, 29 (7), 1055- 1068.
Limmun, W., Chomtee, B., & Borkowski, J. (2019). Constructing model robust mixture designs via weighted G-optimality criterion. International Journal of Industrial Engineering Computations, 10 (4), 473–490.
McCall, J. (2005). Genetic algorithms for modelling and optimization. Journal of Computational and Applied Mathematics, 184 (1), 205–222.
Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs. Springer- Verlag, Berlin.
Ming Tan, C., & Raghvan, N. (2008). A framework to practical predictive maintenance modeling for multi-state systems. Reliability Engineeing and System Safety, 93 (8), 1138-1150.
Moghaddam, K.S., & Usher, J.S. (2011). Sensitivity analysis and comparison of algorithms in preventive maintenance and replacement scheduling optimization models. Computers & Industrial Engineering, 61 (1), 64-75.
Neves, M.L., Santiago, L.P., & Maia, C.A. (2011). A condition-based maintenance policy and input parameters estimation for deteriorating systems under periodic inspection. Computers & Industrial Engineering, 61 (3), 503–511.
Srivastava, N.K., & Mondal, S. (2015). Predictive maintenance using modified FMECA method. International Journal of Productivity and Quality Management, 16 (3), 267–280.
Srivastava, N.K., & Mondal, S. (2016). Development of predictive maintenance model for N- component repairable system using NHPP models and system availability concept, Global Business Review, 17 (1), 105–115.
Srivastava, N.K., Mondal, S., Chatterjee, N., & Parihar, S. (2018). Identifying critical factors for various maintenance policies: A study on Indian manufacturing sector. International Journal of Productivity and Quality Management, 25 (1), 41-63.
Tsarouhas, P.H. (2015). Evaluation of maintenance management through the overall equipment effectiveness of a yogurt production line in a medium-sized Italian company. International Journal of Productivity and Quality Management, 16 (3), 298–311.
Wang, M., Yu, G., & Yu, D. (2011). Mining typical features for highly cited papers. Scientometrics, 87 (3), 695-706.
You, M.Y., Li, H., & Meng, G. (2011). Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions. Reliability Engineering and System Safety, 96 (5), 590–598.


Original Scientific Paper