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.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".