Long-term mine planning: A survey of classical, hybrid and artificial intelligence-based methods

• Main Authors: NURUL ASYIKEEN BINTE AZHAR, GUNAWAN, Aldy, CHENG, Shih-Fen, LEONARDI, Erwin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Optimization; deterministic; stochastic; meta-heuristic; hybrid; artificial intelligence; literature review; open-pit mining; underground mining; mine planning; Agricultural and Resource Economics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9610; https://ink.library.smu.edu.sg/context/sis_research/article/10610/viewcontent/LT_minePlanning_av.pdf
• Institution: Singapore Management University

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or (meta) heuristic methods — toward hybrid methods. They are compared through the scope of the problem formulation and discussed via solution quality, efficiency, and gaps. We finally conclude with opportunities to incorporate artificial intelligence (AI)-based methods due to paucity, multiple operational uncertainties simultaneously, sustainability indicator quantification, and benchmark instances.