An efficient method for optimizing nested open pits with operational bottom space
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
Abstract Determining a set of nested pits to support the design of an open pit mine that leads to high economic value is crucial for the strategic planning of these operations; thus, practitioners rely on optimization methods for finding high‐value solutions. However, current approaches are not sufficient as they lack at least one of the following features: fast computations of optimal solutions, good geometric properties, and nestedness of the pits. In this work, we propose an optimization model to address the problem of determining multiple nested pits by introducing a cost‐based penalty for not meeting precedence constraints linked to a minimum bottom width. Using penalties instead of constraints is novel and turns out to have several advantages. First, the constraint matrix is totally unimodular; thus, the problem can be solved efficiently. Second, the model can be parameterized to generate nested pits. Therefore, our model is the first published model that is efficient, can be solved to optimality, preserves the nestedness of the solutions, and produces geometries more amenable for mine design, without the need for heuristics. Finally, we devise an iterative method that profits from the nestedness of the solutions to speed up the resolution and test the model in three different data sets, with different geometrical and cost parameters for a total of 135 different instances. The results show that the geometry of the bottom pits is indeed improved and that we can solve the problems up to optimality up to 80% faster than an off‐the‐shelf solver.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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