Solving the area-restricted harvest-scheduling model using the branch and bound algorithm
Bibliographic record
Abstract
There are two broad approaches to the discrete optimization problem of harvest scheduling with adjacency constraints: the unit-restricted model and the area-restricted model. In this paper two formulations of the area-restricted model are solved using the branch and bound algorithm. These formulations are tested on a set of approximately 30 tactical planning problems that range in size from 346 to 6093 polygons scheduled over three time periods. Our results show that these formulations can be used to solve small- and medium-sized harvest-scheduling problems optimally, or near-optimally, within reasonable periods of computing time. Solution quality and computing time were found to be sensitive to the initial age-class distribution of the forest and the mean polygon size relative to the maximum opening size. In addition, computation of the opening size constraints was found to be very sensitive to the maximum number of polygons in an opening constraint.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".