On the Importance of Integrating Transportation Costs into Tactical Forest Harvest Scheduling Model
Bibliographic record
Abstract
In tactical forest management planning, the decisions required to meet the strategic plan are made, and these include: i) scheduling of spatially explicit harvest-blocks; ii) construction of a road-network required to access these blocks; and iii) transportation costs within the tactical forest planning area (hereafter only referred to as transportation costs) that emerge from the first two decisions. These three decisions are interdependent and should therefore be integrated in any optimization model. At present, this integration is not fully made. This is because: i) the integrated model is NP-hard, and exact solutions are not feasible for large and medium-sized forests; and ii) metaheuristic search algorithms, which can be used on larger forests, have not integrated transportation costs realistically.The economic consequences of not integrating transportation costs into tactical planning models has not been quantified and evaluated by researchers; and the objective of this paper is to fill this gap in knowledge. To this end, an exact solution approach is used to solve and compare two integrated models: i) a model in which transportation costs are included in the objective function, and b) a model in which transportation costs are excluded from the objective function. The models were applied to three forests ranging in area from 6628 to 19,677 ha.Results show that: i) the model which included transportation costs yielded solutions with major reductions in both transportation and total costs; and ii) that, as the forests to which the model was applied tripled in area (from 6628 ha to 19,677 ha), the percent reduction in total costs increased disproportionately – more than fivefold (from 3.9% to 21%). These results are important, for they indicate that the integration of transportation costs into a tactical planning model is of major economic consequence.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".