Incorporating geological and equipment performance uncertainty while optimising short-term mine production schedules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Short-term production scheduling in open pit mining consists of defining the extraction sequence and process allocation of mineralised material over time-scales of either months, weeks, or days. An effective short-term production schedule ensures compliance with the production targets and restrictions imposed by the long-term plan. The method proposed herein outlines a new approach to simultaneously optimise the short-term production sequence with the mobile equipment allocation plan while incorporating both material grade and equipment performance uncertainty. A new simulation methodology is introduced to generate more realistic equipment performance scenarios, as well as a new concept of including ramp positions in the formulation to efficiently facilitate minable extraction patterns. This short-term model is bench-marked against a conventional design at one of the largest copper mines in the world, and the results show improved production target compliance by delivering more consistent ore quantity and quality to each processing destination, and a physical extraction sequence that has a greater likelihood of being realised in the face of equipment performance and truck cycle time uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it