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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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    2. 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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