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The Application of Stochastic Mine Production Scheduling in the Presence of Geological Uncertainty

Author

Listed:
  • Devendra Joshi

    (Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India)

  • Hamed Gholami

    (Department of Manufacturing and Industrial Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Hitesh Mohapatra

    (School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India)

  • Anis Ali

    (Department of Management, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Dalia Streimikiene

    (Kaunas Faculty, Vilnius University, Muitines 8, LT-44280 Kaunas, Lithuania)

  • Susanta Kumar Satpathy

    (Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi 522213, Andhra Pradesh, India)

  • Arvind Yadav

    (Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India)

Abstract

The scheduling of open-pit mine production is a large-scale, mixed-integer linear programming problem that is computationally expensive. The purpose of this study is to create a computationally efficient algorithm for solving open-pit production scheduling problems with uncertain geological parameters. To demonstrate the effectiveness of the proposed research, a case study of an Indian iron ore mine is presented. Multiple realizations of the resource models were developed and integrated within the stochastic production scheduling framework to capture uncertainty and incorporate it into the mine plan. In this case study, two hybrid methods were developed to evaluate their performance. Model 1 is a combined branch and cut with the longest path, whereas Model 2 is a sequential parametric maximum flow and branch and cut. The results show that both methods produce similar materials, ore, metal, and risk profiles; however, Model 2 generates slightly more (4 percent) discounted cash flow from this study mine than Model 1. The results also show that Model 2’s computational time is 46.64 percent less than that of Model 1.

Suggested Citation

  • Devendra Joshi & Hamed Gholami & Hitesh Mohapatra & Anis Ali & Dalia Streimikiene & Susanta Kumar Satpathy & Arvind Yadav, 2022. "The Application of Stochastic Mine Production Scheduling in the Presence of Geological Uncertainty," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9819-:d:883763
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    References listed on IDEAS

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    1. Lamghari, Amina & Dimitrakopoulos, Roussos, 2016. "Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty," European Journal of Operational Research, Elsevier, vol. 250(1), pages 273-290.
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    4. Krzysztof Skrzypkowski, 2019. "The Influence of Room and Pillar Method Geometry on the Deposit Utilization Rate and Rock Bolt Load," Energies, MDPI, vol. 12(24), pages 1-15, December.
    5. Chatterjee, Snehamoy & Sethi, Manas Ranjan & Asad, Mohammad Waqar Ali, 2016. "Production phase and ultimate pit limit design under commodity price uncertainty," European Journal of Operational Research, Elsevier, vol. 248(2), pages 658-667.
    6. Gilani, Seyyed-Omid & Sattarvand, Javad & Hajihassani, Mohsen & Abdullah, Shahrum Shah, 2020. "A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty," Resources Policy, Elsevier, vol. 68(C).
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    Cited by:

    1. Nelis, Gonzalo & Morales, Nelson & Jelvez, Enrique, 2023. "Optimal mining cut definition and short-term open pit production scheduling under geological uncertainty," Resources Policy, Elsevier, vol. 81(C).

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