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A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning

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  • Noriega, Roberto
  • Pourrahimian, Yashar

Abstract

The significant increase in data availability and high-computing power and innovations in real-time monitoring systems enable the technological transformation of the mining industry. Artificial Intelligence (AI) and data-driven methods are becoming appealing solutions to tackle different challenges in mining operations where an increasingly larger body of research is being published. Strategic mine planning is one of the areas that can be greatly enhanced with the adaptation of AI techniques to make intelligent data-driven decisions. This paper presents a systematic literature review to identify research trends in this field both in the specific area of application and the AI technique used. Papers from popular scientific databases were compiled and categorized into three main identified research areas in this field: Production Planning and Scheduling, Equipment Management and Grade Control, and individual AI techniques were catalogued. The results indicated an exponential growth in the general number of publications, where the most consolidated techniques across all applications were Genetic Algorithms and Discrete Simulation.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001751
    DOI: 10.1016/j.resourpol.2022.102727
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    References listed on IDEAS

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    1. Parag Pendharkar & James Rodger, 2000. "Nonlinear programming and genetic search application for production scheduling in coal mines," Annals of Operations Research, Springer, vol. 95(1), pages 251-267, January.
    2. Amina Lamghari & Roussos Dimitrakopoulos & Jacques A Ferland, 2014. "A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(9), pages 1305-1314, September.
    3. 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.
    4. Montiel, Luis & Dimitrakopoulos, Roussos, 2015. "Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach," European Journal of Operational Research, Elsevier, vol. 247(1), pages 166-178.
    5. Amina Lamghari & Roussos Dimitrakopoulos & Jacques Ferland, 2015. "A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines," Journal of Global Optimization, Springer, vol. 63(3), pages 555-582, November.
    6. Yuanyuan Pu & Derek B. Apel & Alicja Szmigiel & Jie Chen, 2019. "Image Recognition of Coal and Coal Gangue Using a Convolutional Neural Network and Transfer Learning," Energies, MDPI, vol. 12(9), pages 1-11, May.
    7. Danish, Abid Ali Khan & Khan, Asif & Muhammad, Khan & Ahmad, Waqas & Salman, Saad, 2021. "A simulated annealing based approach for open pit mine production scheduling with stockpiling option," Resources Policy, Elsevier, vol. 71(C).
    8. Amin Mousavi & Erhan Kozan & Shi Qiang Liu, 2016. "Comparative analysis of three metaheuristics for short-term open pit block sequencing," Journal of Heuristics, Springer, vol. 22(3), pages 301-329, June.
    9. Nourali, Hamidreza & Osanloo, Morteza, 2019. "Mining capital cost estimation using Support Vector Regression (SVR)," Resources Policy, Elsevier, vol. 62(C), pages 527-540.
    10. Ahmadi, Mohammad Reza & Shahabi, Reza Shakoor, 2018. "Cutoff grade optimization in open pit mines using genetic algorithm," Resources Policy, Elsevier, vol. 55(C), pages 184-191.
    11. Ashish Kumar & Roussos Dimitrakopoulos & Marco Maulen, 2020. "Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1795-1811, October.
    12. Alexandra M. Newman & Enrique Rubio & Rodrigo Caro & Andrés Weintraub & Kelly Eurek, 2010. "A Review of Operations Research in Mine Planning," Interfaces, INFORMS, vol. 40(3), pages 222-245, June.
    13. 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.
    14. Siami-Irdemoosa, Elnaz & Dindarloo, Saeid R., 2015. "Prediction of fuel consumption of mining dump trucks: A neural networks approach," Applied Energy, Elsevier, vol. 151(C), pages 77-84.
    15. Mohammadi, Sadjad & Kakaie, Reza & Ataei, Mohammad & Pourzamani, Eshagh, 2017. "Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm (ICA)," Resources Policy, Elsevier, vol. 51(C), pages 39-48.
    16. M Kumral & P A Dowd, 2005. "A simulated annealing approach to mine production scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 922-930, August.
    17. 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).
    18. Ahmadi, Mohammad Reza & Bazzazi, Abbas Aghajani, 2019. "Cutoff grades optimization in open pit mines using meta-heuristic algorithms," Resources Policy, Elsevier, vol. 60(C), pages 72-82.
    19. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung & Bui, Thu-Thuy & Nguyen, Nga & Vu, Diep-Anh & Mahesh, Vinyas & Moayedi, Hossein, 2020. "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm," Resources Policy, Elsevier, vol. 66(C).
    20. 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.
    21. Lamghari, Amina & Dimitrakopoulos, Roussos, 2016. "Progressive hedging applied as a metaheuristic to schedule production in open-pit mines accounting for reserve uncertainty," European Journal of Operational Research, Elsevier, vol. 253(3), pages 843-855.
    22. 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.
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    1. Joaquin Vespignani & Russell Smyth, 2024. "Artificial intelligence investments reduce risks to critical mineral supply," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Hazrathosseini, Arman & Moradi Afrapoli, Ali, 2023. "The advent of digital twins in surface mining: Its time has finally arrived," Resources Policy, Elsevier, vol. 80(C).
    3. Zhang, Zhouyi & Song, Yi & Cheng, Jinhua & Zhang, Yijun, 2023. "Effects of heterogeneous ICT on critical metal supply: A differentiated perspective on primary and secondary supply," Resources Policy, Elsevier, vol. 83(C).

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