An optimization model for annual harvest planning
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
The problem we consider is annual harvesting planning from the perspective of Swedish forest companies. The main decisions deal with which areas to harvest during an annual period so that the wood-processing facilities receive the required amount of assortments. Each area has a specific size and composition of assortments, and the choice of harvesting areas affects the production level of different assortments. We need to decide which harvest team to use for each area, considering that each team has different skills, home base, and production capacities. Also, the weather and road conditions vary during the year. Some roads cannot be used during certain time periods and others should be avoided. The road maintenance cost varies during the year. Also, some areas cannot be harvested during certain periods. Overall decisions about transportation and storage are also included. In this paper, we develop a mixed integer programming model for the problem. There are binary variables associated with harvesting, allocation of teams, and road-opening decisions. The other decisions are represented by continuous variables. We solve this problem directly with CPLEX 8.1 within a practical solution time limit. Computational results from a major Swedish forest company are presented.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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