An evaluation of the simulated annealing algorithm for solving the area-restricted harvest-scheduling model against optimal benchmarks
Bibliographic record
Abstract
A common approach for incorporating opening constraints into harvest scheduling is through the area-restricted model. This model is used to select which stands to include in each opening while simultaneously determining an optimal harvest schedule over multiple time periods. In this paper we use optimal benchmarks from a range of harvest scheduling problem instances to test a metaheuristic algorithm, simulated annealing, that is commonly used to solve these problems. Performance of the simulated annealing algorithm was assessed over a range of problem attributes such as the number of forest polygons, age-class distribution, and opening size. In total, 29 problem instances were used, ranging in size from 1269 to 36 270 binary decision variables. Overall, the mean objective function values found with simulated annealing ranged from approximately 87% to 99% of the optima after 30 min of computing time, and a moderate downward trend of the relationship between problem size and solution quality was observed.
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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 | 0.005 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".