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A simulation based optimization approach for spare parts forecasting and selective maintenance

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  • Sharma, Pankaj
  • Kulkarni, Makarand S
  • Yadav, Vikas

Abstract

Equipment of the Army encounters various modes of exploitation depending on the scenario in which it is used. Typically, the missions are followed by intervals which can be used for maintenance. This is a suitable condition for employment of selective maintenance strategy. However, this maintenance interval is bound by the constraints of time, resources and desired reliability before the start of the next mission. This calls for optimization of maintenance activities that can be fitted into the maintenance break. There is also a requirement of having a forecasting technique for reducing the supply lead times. This paper lays out a methodology to use simulation for predicting failures in the army equipment. A Genetic Algorithm (GA) based approach is then used for optimizing the maintenance activities before the start of the maintenance break. The process of Simulation plus GA Optimization is automated using a program in MATLAB. The novelty of the work lies in modifying the process of Simulation and GA Optimization to suit the exact modus operandi employed by the Army in deploying equipment for peace, training exercise and war (mission with or without some maintenance break) separately. In addition to optimizing the maintenance activities, the methodology also helps in forecasting the requirement of spare parts both before and during the mission.

Suggested Citation

  • Sharma, Pankaj & Kulkarni, Makarand S & Yadav, Vikas, 2017. "A simulation based optimization approach for spare parts forecasting and selective maintenance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 274-289.
    • Handle: RePEc:eee:reensy:v:168:y:2017:i:c:p:274-289
    DOI: 10.1016/j.ress.2017.05.013
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    Cited by:

    1. Liu, Lujie & Yang, Jun & Kong, Xuefeng & Xiao, Yiyong, 2022. "Multi-mission selective maintenance and repairpersons assignment problem with stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Diallo, Claver & Venkatadri, Uday & Khatab, Abdelhakim & Liu, Zhuojun, 2018. "Optimal selective maintenance decisions for large serial k-out-of-n: G systems under imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 234-245.
    3. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Wenbin Cao & Xisheng Jia & Yu Liu & Qiwei Hu & Jianmin Zhao, 2019. "Selective maintenance optimisation considering random common cause failures and imperfect maintenance," Journal of Risk and Reliability, , vol. 233(3), pages 427-443, June.
    5. Vedpal Arya & S. G. Deshmukh & Naresh Bhatnagar, 2019. "Product quality in an inclusive manufacturing system: some considerations," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2871-2884, December.
    6. Wu, Shaomin & Do, Phuc, 2017. "Editorial," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 1-3.
    7. Boram Choi & Jong Hwan Suh, 2020. "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea," Sustainability, MDPI, vol. 12(15), pages 1-20, July.

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