Coordination between strategic forest management and tactical logistic and production planning in the forestry supply chain
Bibliographic record
Abstract
Abstract In this paper, we study the coordination mechanism in the forestry supply chain between strategic forest management and tactical production planning. We first formulate an integrated model to establish a theoretical benchmark for performance of the entire supply chain. It is a mixed integer programming model that involves harvesting, bucking, transportation, production, and sales decisions for both tactical and strategic planning levels. We then present two sequential approaches S‐A and S‐B where the coordination is done through internal pricing. S‐A is the approach currently used in practice where harvesting in the forest is the main driver of the supply chain activities and internal pricing is introduced to control bucking decision in a separate stage. In contrast, S‐B takes downstream demand information into consideration and internal pricing directly influences harvesting decision in the first stage. In order to find the appropriate setting of internal pricing that leads to the system optimum, we suggest two heuristics H‐I and H‐II. The internal pricing in H‐I is based on dual values and in H‐II, it is derived from a Lagrangian decomposition. A real‐life case study in the Chilean forestry industry is used to compare the results of different approaches. It is shown that the new sequential approach S‐B generates as good feasible solution as that obtained from the integrated approach but in much less time. Both heuristics H‐I and H‐II bring about near‐optimal feasible solutions. H‐II also provides optimistic bound of the optimal objective function value, which can be used as a measure of the solution quality.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".