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Delay-time-based preventive maintenance modelling for a production plant: a case study in a steel mill

Author

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  • Fei Zhao

    (University of Science and Technology Beijing, Beijing, China)

  • Wenbin Wang

    (University of Science and Technology Beijing, Beijing, China
    Manchester Metropolitan University, Manchester, UK)

  • Rui Peng

    (University of Science and Technology Beijing, Beijing, China)

Abstract

This paper presents a case study of delay-time-based preventive maintenance (PM) modelling for a production plant system. Since production stoppages caused by waiting for raw materials provide windows to inspect and maintain the system, these production stoppages can be incorporated into the PM model. Considering the nature of different defects that can cause failures, two types of defects are modelled: small and large defects. Small defects are normally dealt with during production stoppages, but both small and large defects can be dealt with over a longer duration during PM. The parameters of the model are estimated using the maximum-likelihood method based on the real data. The model aims to find the optimal PM interval by minimizing the expected total downtime within an overhaul cycle. Management suggestions are also recommended.

Suggested Citation

  • Fei Zhao & Wenbin Wang & Rui Peng, 2015. "Delay-time-based preventive maintenance modelling for a production plant: a case study in a steel mill," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(12), pages 2015-2024, December.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:12:p:2015-2024
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    Citations

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    Cited by:

    1. Song Jiu, 2021. "A two-phase approach for integrating preventive maintenance with production and delivery in an unreliable coal mine," Journal of Heuristics, Springer, vol. 27(6), pages 991-1020, December.
    2. Gao, Kaiye & Peng, Rui & Qu, Li & Wu, Shaomin, 2020. "Jointly optimizing lot sizing and maintenance policy for a production system with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Zhao, Xian & He, Zongda & Wu, Yaguang & Qiu, Qingan, 2022. "Joint optimization of condition-based performance control and maintenance policies for mission-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Zahedi-Hosseini, Farhad & Scarf, Philip & Syntetos, Aris, 2017. "Joint optimisation of inspection maintenance and spare parts provisioning: a comparative study of inventory policies using simulation and survey data," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 306-316.
    5. Yang, Li & Ye, Zhi-sheng & Lee, Chi-Guhn & Yang, Su-fen & Peng, Rui, 2019. "A two-phase preventive maintenance policy considering imperfect repair and postponed replacement," European Journal of Operational Research, Elsevier, vol. 274(3), pages 966-977.
    6. Arturo Realyvásquez-Vargas & Karina Cecilia Arredondo-Soto & Julio Blanco-Fernandez & Joanna Denisse Sandoval-Quintanilla & Emilio Jiménez-Macías & Jorge Luis García-Alcaraz, 2020. "Work Standardization and Anthropometric Workstation Design as an Integrated Approach to Sustainable Workplaces in the Manufacturing Industry," Sustainability, MDPI, vol. 12(9), pages 1-22, May.
    7. Peng, Rui & Liu, Bin & Zhai, Qingqing & Wang, Wenbin, 2019. "Optimal maintenance strategy for systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 624-632.
    8. Zhang, Fengxia & Shen, Jingyuan & Ma, Yizhong, 2020. "Optimal maintenance policy considering imperfect repairs and non-constant probabilities of inspection errors," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    9. Qiu, Qingan & Cui, Lirong, 2019. "Optimal mission abort policy for systems subject to random shocks based on virtual age process," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 11-20.

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