Optimization of operational level transportation planning in forestry: a review
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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