A new heuristic memory-based simulated annealing approach applied to mine production scheduling problem
Bibliographic record
Abstract
Mine production scheduling serves to maximise the net present value of a mine by solving three interconnected sub-problems: a) extraction sequence of mining blocks; b) ore-waste discrimination; c) production rates. Even though potential of scheduling is well-recognised, some issues have not been resolved: 1) the sub-problems given above are solved in a sequential fashion rather than simultaneously that leads to sub-optimality; 2) the number of decision variables and constraints can easily be over millions. In this paper, a notion of memory is introduced into simulated annealing (SA) to solve such a large problem efficiently. A new heuristic memory is added to SA such that better search results are obtained faster. At each iteration, the heuristic and the objective function are recorded. If their correlation is high, heuristic is also incorporated into the objective function. A case study demonstrated the proposed method performs faster than the original simulated annealing algorithm.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".