Optimal preventive maintenance interval for a Crankshaft balancing machine under reliability constraint using Bonobo Optimizer

  • Amit Kumar Das
  • Dilip Kumar PratiharEmail author
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)


Profit-making is one of the main aims of any manufacturing unit and minimization of the overall maintenance cost can help to reach this goal. However, this should be achieved without any compromise with various machinery reliability constraints. Often, due to the busy production schedule, maintenance job is postponed, which leads to the higher production cost for frequent breakdowns, and others. These can be mitigated by performing preventive maintenance (PM), but it has to be done in an optimal sense. An optimized PM interval should minimize the total maintenance cost, while satisfying the lower bound reliability constraints. Most of the PM models available in literature do not address this aspect, especially for the real industrial circumstances. Crankshaft balancing machine is an example of such a system, where PM interval is to be optimized for the better production rate and overall maintenance cost minimization. To solve this problem, meta-heuristic techniques, such as genetic algorithm (GA), particle swarm optimization (PSO) have been implemented. However, in this paper, a recently-developed optimization method, namely Bonobo Optimizer (BO), has been applied to determine the optimal PM interval with the minimized total maintenance cost. In this experiment, BO is able to yield better results compared to that of the GA and PSO, and a considerable amount of cost reduction has been achieved using this technique.


Maintenance cost PM interval Reliability constraint Bonobo Optimizer 


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  1. 1.
    Talukder, S., Knapp, G.M.: Equipment assignment to multiple overhaul blocks in series systems. Journal of Quality in Maintenance Engineering 8(4), 319-330 (2002).CrossRefGoogle Scholar
  2. 2.
    Li, L., Ni, J.: Reliability estimation based on operational data of manufacturing systems. Quality and Reliability Engineering International 24(7), 843-854 (2008).CrossRefGoogle Scholar
  3. 3.
    Ebeling, C.E.: An introduction to reliability and maintainability engineering. Tata McGraw-Hill Education, New York (2004)Google Scholar
  4. 4.
    Nguyen, D., Bagajewicz, M.: Optimization of preventive maintenance scheduling in processing plants. In: Braunschweig, B., Joulia, X. (eds.) Computer Aided Chemical Engineering, vol. 25. pp. 319-324. Elsevier, (2008)Google Scholar
  5. 5.
    Jardine, A.K.S., Buzacott, J.A.: Equipment reliability and maintenance. European Journal of Operational Research 19(3), 285-296 (1985). Scholar
  6. 6.
    Sun, J.-W., Xi, L.-F., Du, S.-C., Ju, B.: Reliability modeling and analysis of serial-parallel hybrid multi-operational manufacturing system considering dimensional quality, tool degradation and system configuration. International Journal of Production Economics 114(1), 149-164 (2008). Scholar
  7. 7.
    Pratihar, D.K.: Realizing the Need for Intelligent Optimization Tool. In: Handbook of Research on Natural Computing for Optimization Problems. pp. 1-9. IGI Global, (2016)Google Scholar
  8. 8.
    Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Pattern - directed inference systems. pp. 313-329. Elsevier, (1978)Google Scholar
  9. 9.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human science (MHS’95), pp. 39-43.Google Scholar
  10. 10.
    Kumarappan, N., Suresh, K.: Particle Swarm Optimization Based Approach to Maintenance Scheduling Using Levelized Risk Method. In: 2008 Joint International Conference on Power System Technology and IEEE Power India Conference, 2008, pp. 1-6Google Scholar
  11. 11.
    Chou, J.-S., Le, T.-S.: Reliability-based performance simulation for optimized pavement maintenance. Reliability Engineering & System Safety 96(10), 1402-1410 (2011). Scholar
  12. 12.
    Yare, Y., Venayagamoorthy, G.K.: Optimal maintenance scheduling of generators using multiple swarms-MDPSO framework. Engineering Applications of Artificial Intelligence 23(6), 895-910 (2010). Scholar
  13. 13.
    Wang, C.-H., Lin, T.-W.: Improved particle swarm optimization to minimize periodic preventive maintenance cost for series-parallel systems. Expert Systems with Applications 38(7), 8963-8969 (2011). Scholar
  14. 14.
    Loganathan, M.K., Gandhi, O.P.: Maintenance cost minimization of manufacturing systems using PSO under reliability constraint. International Journal of System Assurance Engineering and Management 7(1), 47-61 (2016). Scholar
  15. 15.
    Das, A.K., Pratihar, D.K.: A New Bonobo Optimizer (BO) for Real-Parameter Optimization. Cognitive computation (Under review) (2018).Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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