Optimizing Mineral Value Chain with Market Uncertainty Using Benders Decomposition
Bibliographic record
Abstract
A Benders decomposition-based method is developed to simultaneously optimize upstream and downstream operations of a mineral value chain. In each iteration of the proposed method, the mineral value chain optimization model is decomposed to a master problem that only includes the variables that determine the upstream mine production schedule, and a subproblem that includes all other variables that define the downstream material flow and processing plan. In order to reduce the master problem in each iteration, mining blocks representing mineral deposits are dynamically aggregated based on the dual solution of the subproblem. The production schedule obtained based on the aggregated scheduling units is then improved through a moving-window amelioration method. By observing the results of a series of numerical tests, we show that the proposed method efficiently optimizes a mineral value chain by synchronizing the upstream mine production scheduling as well as the downstream material flow and process planning. The numerical tests also show that ignoring market uncertainty results in profits being underestimated because of the underestimated value of low-grade material. To adapt to market uncertainty, the stochastic optimizer suggests greater investment to increase capacity in the processing plant and a different long-term mine production schedule.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".