Benefits and Requirements of Mathematical Optimization in the Allocation of Wood to a Network of Forest Product Mills
Bibliographic record
Abstract
Supply planning is a challenge for the forest industry in the context of natural forests characterized by heterogeneity among raw materials. Several mathematical models have been proposed in the literature to support forest planning, though few have been used by companies. The complexity of the natural environment and the expertise required to use these models limit their application. Nevertheless, these tools can significantly improve profitability. Three main elements were analyzed to assess benefits fostered by computer-optimized planning: (i) assessing the potential of implementing mathematical optimization in companies, particularly by pinpointing the additional resources necessary; (ii) determining the benefits of mathematical optimization to support planning decisions in an industrial context; and (iii) analyzing the impact of variation in information precision. LogiLab, an optimization software was used to find the optimal allocation of raw materials to an industrial network of five mills. The plan produced using optimization was compared to the plan generated by company personnel. The optimized plan generated a nearly 20% greater net profit than the current planning method. This difference was in part due to the more efficient allocation of raw materials to mills. It also highlighted numerous benefits, including a 50% reduction in the time required to produce plans. Furthermore, if attributed volume can be distributed among sawmills, it would be possible to save CAD 3.21/m3 using optimized plans, greatly exceeding initial costs related to system implementation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".