A hierarchical planning system to assess the impact of operational-level flexibility on long-term wood supply
Bibliographic record
Abstract
Operational-level flexibility in the choice of silvicultural treatments allows mitigatation of the impact of uncertain demand on supply chain performance. Silvicultural treatments dictate the species and quantity harvested from forest stands. However, their impact on wood supply sustainability is not clearly understood. This study proposes a simulation–optimization system to model hierarchical forest management planning with an objective to examine the impact. The system consists of mathematical models to develop hierarchical plans, i.e., strategic, tactical, and operational. In the system, the strategic model is first solved to determine the annual allowable cut. Next, the tactical model allocates cutblocks to annual plans, also prescribing silvicultural treatments. The subsequent operational-level model generates monthly plans in a rolling planning basis to satisfy prevailing demand. Upon execution of all operational-level plans, the forest inventory is updated and the change in annual allowable cut is evaluated. The system was implemented in a case study in Quebec, Canada. Permitting silvicultural flexibility at the operational level led to profit improvements between 2% and 3.7% over a 100 year horizon. Significant impact on long-term wood supply was not observed in this case. The proposed system can help forest planners and supply chain managers better integrate their respective needs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".