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Analysis of maintenance cost for an asset using the genetic algorithm

Author

Listed:
  • Mohammad Asjad

    (Jamia Millia Islamia)

  • Shahbaz Khan

    (Jamia Millia Islamia)

Abstract

Nowadays, almost every firm focuses to beat the global competition across the worldwide. In order to deal with such situation, companies are undertaking efforts to improve the productivity of their products but at the minimum possible cost. Asset management is one of the ways to enhance the productivity under cost constraint which may also be seen as the management strategy for different the phases of asset life cycle. Operations and maintenance is one of the important phases of asset life cycle that can be focussed to improve the productivity. This phase may extend the equipment life, improves availability and retains them in healthy positions. But at the same time, frequent maintenance actions may increase the maintenance cost thereby increase the life cycle cost of a product. The maintenance cost only includes the preventive and corrective maintenance cost and which may in-turn depend upon the scheduled maintenance interval. Thus, a trade-off between maintenance actions and operational objectives (i.e. availability, etc.) is required to minimize the maintenance cost. In this paper, the genetic algorithm is applied to optimize the maintenance cost for higher performance (i.e. availability). A case study is taken into consideration for implementing the GA to optimize the objective function. The three different cases are presented, in the first case, subassemblies are repaired during maintenance action(s); in the second case subassemblies are repaired in preventive maintenance action and while replaced in corrective maintenance action; in the last case, the subassemblies are replaced in both kind of maintenance. In order to check the robustness of the solution, the sensitivity analysis is also performs and that validates the strength of the solution methodology.

Suggested Citation

  • Mohammad Asjad & Shahbaz Khan, 2017. "Analysis of maintenance cost for an asset using the genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 445-457, June.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0448-9
    DOI: 10.1007/s13198-016-0448-9
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    References listed on IDEAS

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    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2013. "Risk-based design of process systems using discrete-time Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 5-17.
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    Cited by:

    1. Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    2. Sadia Samar Ali & Rajbir Kaur & Shahbaz Khan, 2023. "Evaluating sustainability initiatives in warehouse for measuring sustainability performance: an emerging economy perspective," Annals of Operations Research, Springer, vol. 324(1), pages 461-500, May.

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