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A new MIP model for mine equipment scheduling by minimizing maintenance cost

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  • Topal, Erkan
  • Ramazan, Salih

Abstract

Mining investment has been recognized as capital intensive due mainly to the cost of large equipment. Equipment capital costs for a given operation are usually within the order of hundreds of million dollars but may reach to billion dollars for large companies operating multiple mines. Such large investments require the optimum usage of equipment in a manner that the operating costs are minimized and the utilization of equipment is maximized through optimal scheduling. This optimum usage is required to ensure that the business remains sustainable and financially stable. Most mining operations utilize trucks to haul the mined material. Maintenance is one of the major operating cost items for these fleets as it can reach approximately one hundred million dollars yearly. There is no method or application in the literature that optimizes the utilization for truck fleet over the life of mine. A new approach based on mixed integer programming (MIP) techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over a multi-year time horizon to minimize maintenance cost. The model uses the truck age (total hours of usage), maintenance cost and required operating hours to achieve annual production targets to produce an optimum truck schedule. While this paper focuses on scheduling trucks for mining operation, concept can be used in most businesses using equipment with significant maintenance costs. A case study for a large scale gold mine showed an annual discounted (10% rate) maintenance cost saving of over $2M and more than 16% ($21M) of overall maintenance cost reduction over 10 years of mine life, compared with the spreadsheet based approach used currently at the operation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:2:p:1065-1071
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    References listed on IDEAS

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    1. Zeng, Lanyan & Liu, Shi Qiang & Kozan, Erhan & Corry, Paul & Masoud, Mahmoud, 2021. "A comprehensive interdisciplinary review of mine supply chain management," Resources Policy, Elsevier, vol. 74(C).
    2. Janiak, Adam & Janiak, Władysław A. & Krysiak, Tomasz & Kwiatkowski, Tomasz, 2015. "A survey on scheduling problems with due windows," European Journal of Operational Research, Elsevier, vol. 242(2), pages 347-357.
    3. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    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. Matamoros, Martha E. Villalba & Dimitrakopoulos, Roussos, 2016. "Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations and blending restrictions," European Journal of Operational Research, Elsevier, vol. 255(3), pages 911-921.
    6. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    7. 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.
    8. Moradi Afrapoli, Ali & Tabesh, Mohammad & Askari-Nasab, Hooman, 2019. "A multiple objective transportation problem approach to dynamic truck dispatching in surface mines," European Journal of Operational Research, Elsevier, vol. 276(1), pages 331-342.
    9. Pengchao Zhang & Xiang Liu & Zebang Yi & Qiuzhi He, 2024. "Improved Multi-Objective Beluga Whale Optimization Algorithm for Truck Scheduling in Open-Pit Mines," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    10. Jiskani, Izhar Mithal & Cai, Qingxiang & Zhou, Wei & Ali Shah, Syed Ahsan, 2021. "Green and climate-smart mining: A framework to analyze open-pit mines for cleaner mineral production," Resources Policy, Elsevier, vol. 71(C).
    11. Nakousi, C. & Pascual, R. & Anani, A. & Kristjanpoller, F. & Lillo, P., 2018. "An asset-management oriented methodology for mine haul-fleet usage scheduling," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 336-344.
    12. Subin Antony Jose & Joy Calhoun & Otoniel B. Renteria & Pedro Mercado & Shinichiro Nakajima & Colton N. Hope & Mario Sotelo & Pradeep L. Menezes, 2024. "Promoting a Circular Economy in Mining Practices," Sustainability, MDPI, vol. 16(24), pages 1-34, December.
    13. Lamghari, Amina & Dimitrakopoulos, Roussos, 2012. "A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty," European Journal of Operational Research, Elsevier, vol. 222(3), pages 642-652.
    14. King, Barry & Goycoolea, Marcos & Newman, A., 2017. "Optimizing the open pit-to-underground mining transition," European Journal of Operational Research, Elsevier, vol. 257(1), pages 297-309.
    15. Chaowasakoo, Patarawan & Seppälä, Heikki & Koivo, Heikki & Zhou, Quan, 2017. "Improving fleet management in mines: The benefit of heterogeneous match factor," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1052-1065.
    16. Xia, Tangbin & Jin, Xiaoning & Xi, Lifeng & Ni, Jun, 2015. "Production-driven opportunistic maintenance for batch production based on MAM–APB scheduling," European Journal of Operational Research, Elsevier, vol. 240(3), pages 781-790.
    17. Zbigniew Krysa & Przemysław Bodziony & Michał Patyk, 2021. "Discrete Simulations in Analyzing the Effectiveness of Raw Materials Transportation during Extraction of Low-Quality Deposits," Energies, MDPI, vol. 14(18), pages 1-19, September.
    18. González-Gorbeña, Eduardo & Qassim, Raad Y. & Rosman, Paulo C.C., 2016. "Optimisation of hydrokinetic turbine array layouts via surrogate modelling," Renewable Energy, Elsevier, vol. 93(C), pages 45-57.
    19. Christina N. Burt & Lou Caccetta, 2014. "Equipment Selection for Surface Mining: A Review," Interfaces, INFORMS, vol. 44(2), pages 143-162, April.
    20. Pérez, Juan & Maldonado, Sebastián & González-Ramírez, Rosa, 2018. "Decision support for fleet allocation and contract renegotiation in contracted open-pit mine blasting operations," International Journal of Production Economics, Elsevier, vol. 204(C), pages 59-69.

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