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A hybrid heuristic algorithm for the open-pit-mining operational planning problem

Author

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  • Souza, M.J.F.
  • Coelho, I.M.
  • Ribas, S.
  • Santos, H.G.
  • Merschmann, L.H.C.

Abstract

This paper deals with the Open-Pit-Mining Operational Planning problem with dynamic truck allocation. The objective is to optimize mineral extraction in the mines by minimizing the number of mining trucks used to meet production goals and quality requirements. According to the literature, this problem is NP-hard, so a heuristic strategy is justified. We present a hybrid algorithm that combines characteristics of two metaheuristics: Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search. The proposed algorithm was tested using a set of real-data problems and the results were validated by running the CPLEX optimizer with the same data. This solver used a mixed integer programming model also developed in this work. The computational experiments show that the proposed algorithm is very competitive, finding near optimal solutions (with a gap of less than 1%) in most instances, demanding short computing times.

Suggested Citation

  • Souza, M.J.F. & Coelho, I.M. & Ribas, S. & Santos, H.G. & Merschmann, L.H.C., 2010. "A hybrid heuristic algorithm for the open-pit-mining operational planning problem," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1041-1051, December.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:2:p:1041-1051
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Franco-Sepúlveda, Giovanni & Del Rio-Cuervo, Juan Camilo & Pachón-Hernández, María Angélica, 2019. "State of the art about metaheuristics and artificial neural networks applied to open pit mining," Resources Policy, Elsevier, vol. 60(C), pages 125-133.
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    4. S.R. Patterson & E. Kozan & P. Hyland, 2016. "An integrated model of an open-pit coal mine: improving energy efficiency decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4213-4227, July.
    5. 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.
    6. Patterson, S.R. & Kozan, E. & Hyland, P., 2017. "Energy efficient scheduling of open-pit coal mine trucks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 759-770.
    7. Zhang, Jian & Dimitrakopoulos, Roussos G., 2017. "A dynamic-material-value-based decomposition method for optimizing a mineral value chain with uncertainty," European Journal of Operational Research, Elsevier, vol. 258(2), pages 617-625.
    8. Coelho, V.N. & Grasas, A. & Ramalhinho, H. & Coelho, I.M. & Souza, M.J.F. & Cruz, R.C., 2016. "An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints," European Journal of Operational Research, Elsevier, vol. 250(2), pages 367-376.
    9. 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.
    10. 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.
    11. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Reis, Agnaldo J.R. & Enayatifar, Rasul & Souza, Marcone J.F. & Guimarães, Frederico G., 2016. "A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment," Applied Energy, Elsevier, vol. 169(C), pages 567-584.
    12. Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.
    13. Huang, Baobin & Tang, Lixin & Baldacci, Roberto & Wang, Gongshu & Sun, Defeng, 2023. "A metaheuristic algorithm for a locomotive routing problem arising in the steel industry," European Journal of Operational Research, Elsevier, vol. 308(1), pages 385-399.
    14. 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.
    15. 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.
    16. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    17. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    18. de Castro Pena, Guilherme & Santos, Andréa Cynthia & Prins, Christian, 2023. "Solving the integrated multi-period scheduling routing problem for cleaning debris in the aftermath of disasters," European Journal of Operational Research, Elsevier, vol. 306(1), pages 156-172.

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