Publication Type
Journal Article
Version
acceptedVersion
Publication Date
7-2024
Abstract
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.
Keywords
Optimization, deterministic, stochastic, meta-heuristic, hybrid, artificial intelligence, literature review, open-pit mining, underground mining, mine planning
Discipline
Agricultural and Resource Economics | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Asia-Pacific Journal of Operational Research
First Page
1
Last Page
31
ISSN
0217-5959
Identifier
10.1142/S0217595924400141
Publisher
World Scientific Publishing
Citation
NURUL ASYIKEEN BINTE AZHAR; GUNAWAN, Aldy; CHENG, Shih-Fen; and LEONARDI, Erwin.
Long-term mine planning: A survey of classical, hybrid and artificial intelligence-based methods. (2024). Asia-Pacific Journal of Operational Research. 1-31.
Available at: https://ink.library.smu.edu.sg/sis_research/9610
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1142/S0217595924400141
Included in
Agricultural and Resource Economics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons