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Integrated planning framework for preventive maintenance grouping: A case study for a conveyor system in the Chilean mining industry

Author

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  • Pablo Viveros
  • Rodrigo Mena
  • Enrico Zio
  • Leonardo Miqueles
  • Fredy Kristjanpoller

Abstract

Increasingly complex production environments pose new challenges and require the development of new methodological approaches in the search for effective and cost-efficient maintenance planning, taking into account their applicability in real industrial environments. In this context, this article proposes an extension of the integrated framework for opportunistic preventive maintenance (PM) planning, proposed originally in Viveros et al. integrating non-negligible execution times of PM activities and time-window tolerances criterion for the generation of opportunistic grouping schemes. This work offers the implementation of the proposed framework in a practical case within the Chilean mining industry, expanding the analysis on scarce resource availability scenarios for PM tasks to be performed on conveyor belts in the grinding process. The proposed planning optimization model is formulated under the mixed-integer linear programming (MILP) paradigm, and searches minimizing the asset unavailability under several tolerance levels for the case study analyzed. The results show a 35% downtime reduction for a maximum tolerance factor of 10% considering an unconstrained maintenance resource scenario, which confirms the ability of the model to increase asset availability, minimizing system interruptions, improving its cost efficiency, and enhancing productivity levels in the mining industry.

Suggested Citation

  • Pablo Viveros & Rodrigo Mena & Enrico Zio & Leonardo Miqueles & Fredy Kristjanpoller, 2023. "Integrated planning framework for preventive maintenance grouping: A case study for a conveyor system in the Chilean mining industry," Journal of Risk and Reliability, , vol. 237(5), pages 1011-1028, October.
    • Handle: RePEc:sae:risrel:v:237:y:2023:i:5:p:1011-1028
    DOI: 10.1177/1748006X211053758
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    References listed on IDEAS

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    1. Northey, S. & Mohr, S. & Mudd, G.M. & Weng, Z. & Giurco, D., 2014. "Modelling future copper ore grade decline based on a detailed assessment of copper resources and mining," Resources, Conservation & Recycling, Elsevier, vol. 83(C), pages 190-201.
    2. Zhu, Wenjin & Castanier, Bruno & Bettayeb, Belgacem, 2019. "A dynamic programming-based maintenance model of offshore wind turbine considering logistic delay and weather condition," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    3. Topal, Erkan & Ramazan, Salih, 2010. "A new MIP model for mine equipment scheduling by minimizing maintenance cost," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1065-1071, December.
    4. Xia, Tangbin & Xi, Lifeng & Zhou, Xiaojun & Lee, Jay, 2012. "Dynamic maintenance decision-making for series–parallel manufacturing system based on MAM–MTW methodology," European Journal of Operational Research, Elsevier, vol. 221(1), pages 231-240.
    5. Lu, Biao & Zhou, Xiaojun, 2017. "Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 116-127.
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