Planning methods and decision support systems in vehicle routing problems for timber transportation: a review
Why this work is in the frame
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Bibliographic record
Abstract
In the forest industry, transportation accounts for a significant part of the operational costs. Reducing transportation costs through advanced planning and improved efficiency has motivated considerable research efforts. A substantial part of the research has been devoted to planning methodology and decision support systems development to solve large complex vehicle routing problems in forest timber or roundwood transportation. This paper reviews the scientific contributions to timber transportation vehicle routing planning methodologies and decision support systems used in case studies found in the literature. The challenges of their deployment are discussed and future research opportunities are presented. Vehicle routing problems in timber transportation differ from general vehicle routing problems in many aspects. This paper also describes the industrial context, in which various timber transportation vehicle routing problems (TTVRPs) are intrinsic. Several attributes are identified that characterize the TTVRPs, for which various planning and solution methodologies have been implemented.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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