Sustainability in Long-Term Surface Mine Planning: A Systematic Review of Operations Research Applications
Bibliographic record
Abstract
The mining industry, critical for global resource supply, has historically been linked to significant environmental degradation and social challenges, including habitat disruption, water and soil contamination, as well as fatalities. But recently, the industry has been undergoing a transformative change to meet evolving environmental and societal expectations. Operations Research (OR) provides essential tools and techniques to optimize decision-making processes in this context. This paper presents a comprehensive review of different OR methods and their applications in balancing economic, social, and environmental objectives in surface mine planning. Of the 63 papers reviewed, 45 were published by researchers from three main countries: Canada, the United States, and Australia, with 73% of these papers appearing in journals. This review highlighted that linear programming (LP), dynamic programming (DP), Lagrangian relaxation (LR), stochastic programming, and metaheuristics are among the most widely applied methods in surface mine planning within a sustainable development (SD) context. By evaluating the strengths and limitations of these techniques, this review offers valuable insights for researchers and practitioners aiming to improve production efficiency and sustainability through advanced planning strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".