Detailed scheduling of forest harvesting at the operational level incorporating decisions on multiple machine assignment
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".