Detailed scheduling of forest harvesting at the operational level incorporating decisions on multiple machine assignment
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
It is crucial to efficiently schedule harvesting activities in order to reduce the delivered cost of logs. Mathematical models have been used to optimize the harvest scheduling at the operational level. However, in the existing literature, the number of machines assigned for each activity at each cut block was not considered as a decision variable. Also, the impact of the slope of cut blocks on the precedence relationship between harvesting activities was not considered in tprevious studies. In this work, a mathematical model is developed with the possibility of assigning multiple machines for the same harvest activity at each cut block, considering the precedence relationship between activities based on the slope of cut blocks in order to minimize the total cost of harvesting. This work is an extension of our previous work on detailed scheduling of harvesting. The model is applied to the harvesting operations of a large forest company in Coastal British Columbia, Canada. The model’s result for operating cost is only 3.3% higher than the lowest possible operating cost. Only one machine has an idle time. For the same case study, the total cost of the developed model is about 34% lower than that of the previous model.
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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 | 0.001 |
| 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