Optimization based planning tools for routing of forwarders at harvest areas
Bibliographic record
Abstract
The forwarding of logs at harvest areas once the harvesting is done is planned manually by experienced operators. To improve their efficiency and simplify the planning we have developed and tested a decision support system at a major Swedish forest company. The system is based on a combination of a geographic information system (GIS), global positioning system (GPS), and optimization routines to solve the underlying vehicle routing problem. The routes for the forwarders are found by using a repeated matching algorithm. The solution time is short, and it is possible to find routes dynamically in a real-time environment. The geographic information required is found by using a GPS together with data obtained from the bucking software in the harvesters. To show the routes and location of the forwarder, we make use of a GIS that is connected to the GPS. We report on a study with savings in the distance travelled of 8% and numerical tests on the solution methodology. We also compare the proposed solution method with some well-known routing methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.001 |
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".