A Sequential Optimization Approach in Tactical Planning for Value Creation in the Forest Products Industry
Bibliographic record
Abstract
Strategic, tactical, and operation-level forest management plans are commonly formulated by forest planners following even-flow yield principles. Although strategic planning ensures a sustained supply of timber over the long term, it disregards individual mills’ requirements, which leads to discrepancy between supply and demand. We hypothesize that a value-based timber allocation decision, which accounts for individual mills’ demands during tactical level planning, reduces such discrepancy by increasing value over the entire supply chain. Three types of linear programming models were constructed: Model A—status quo volume-maximization model, Model B—supply chain net present value-maximization (NPV) model, and Model C—a novel approach with sub-models embedded that maximize the NPV of individual mills in the allocation decision. Our results showed that only 58% of the annual allowable cut was profitable and the mean net revenue per harvested area was $2455 ha−1 using Model A. The respective values using Models B and C were 64% and $3890 ha−1 and 96% and $4040 ha−1, respectively, showing that Model C generated the highest net revenue for all mills. Such a method of value-based sequential optimization (Model C) will be crucial in sustainable use of forest products and sustaining future bioeconomy, particularly for managing mixed species stands that contain timber suitable for manufacturing a wide range of products with different market values.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".