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Medium-Term Rail Scheduling for an Iron Ore Mining Company

Author

Listed:
  • Gaurav Singh

    (CSIRO Mathematics, Informatics and Statistics, Clayton, Victoria 3168, Australia)

  • Rodolfo García-Flores

    (CSIRO Mathematics, Informatics and Statistics, Clayton, Victoria 3168, Australia)

  • Andreas Ernst

    (CSIRO Mathematics, Informatics and Statistics, Clayton, Victoria 3168, Australia)

  • Palitha Welgama

    (Rio Tinto Iron Ore, Operations Center, Perth Domestic Airport, Western Australia 6105, Australia)

  • Meimei Zhang

    (Rio Tinto Iron Ore, Operations Center, Perth Domestic Airport, Western Australia 6105, Australia)

  • Kerry Munday

    (Rio Tinto Iron Ore, Operations Center, Perth Domestic Airport, Western Australia 6105, Australia)

Abstract

In mineral supply chains, medium-term plans are made for scheduling crews, production, and maintenance. These plans must respect constraints associated with loading and unloading, stockyard capacities, fleet capacities, and maintenance and production requirements. Additionally, compliance with grade quality depends on blending minerals from different sources. In this paper, we present an optimization tool developed for a major multinational iron ore mining company to manage the operations of its supply network in the Pilbara region of Western Australia. The tool produces plans for time horizons from a few weeks to two years, while addressing the nonlinearities that blending introduces. The plans our tool produces allow the company to ship a higher amount of iron ore than it did when it followed the plans obtained by its former manual approach. The company’s planners now rely solely on our tool because it has enabled them to schedule up to one million additional tonnes of material per annum and has reduced the planning time from five hours to less than one hour.

Suggested Citation

  • Gaurav Singh & Rodolfo García-Flores & Andreas Ernst & Palitha Welgama & Meimei Zhang & Kerry Munday, 2014. "Medium-Term Rail Scheduling for an Iron Ore Mining Company," Interfaces, INFORMS, vol. 44(2), pages 222-240, April.
    • Handle: RePEc:inm:orinte:v:44:y:2014:i:2:p:222-240
    DOI: 10.1287/inte.1120.0669
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    References listed on IDEAS

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    Cited by:

    1. Lu Chen & Qinghua Gu & Rui Wang & Zhidong Feng & Chao Zhang, 2022. "Comprehensive Utilization of Mineral Resources: Optimal Blending of Polymetallic Ore Using an Improved NSGA-III Algorithm," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    2. 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).
    3. Michelle L. Blom & Christina N. Burt & Adrian R. Pearce & Peter J. Stuckey, 2014. "A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 658-676, November.
    4. LaRoche-Boisvert, Mélanie & Dimitrakopoulos, Roussos & Ferland, Jacques A., 2021. "Simultaneous production scheduling and transportation optimization from mines to port under uncertain material supply," Resources Policy, Elsevier, vol. 73(C).

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