Optimization of operational level transportation planning in forestry: a review
Bibliographic record
Abstract
Transportation of forest products accounts as a major contributor to the total operational costs; hence, its optimization has become an important aspect in supply chain planning. Transportation optimization at the operational level includes decisions related to product flow, storage, pre-processing, and routing and scheduling of vehicles. The decisions and constraints in the model depend on the type of product that is transported. Earlier review articles on forest transportation optimization focused only on log transportation, while in this review paper, products such as logs, biomass, pulp and furniture are considered and their similarities and differences are highlighted. Most of the previous studies focused on optimizing the total cost of transportation, while environmental aspects of truck routing and scheduling in forestry were not considered. Uncertainties in parameters such as supply and demand quantities and transportation time were not explored fully in the models. In addition to storage and truck routing and scheduling, considering pre-processing (e.g. sorting, grinding, blending, bucking) decisions at forest sites, satellite yards and the mills in the models could be done in future studies. It is important that aspects related to truck configuration, type and capacity be considered in the models as there is limited accessibility of large trucks such as large chip vans to forest sites. Management practices such as just-in-time production and vendor-managed inventory systems could be considered in forest supply chain planning. Using big data and business analytics techniques are other new trends that could improve decision-making related to logistics and transportation planning in forestry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".