A detailed mathematical programming model for the optimal daily planning of sawmills
Bibliographic record
Abstract
The daily production planning of sawmills is a critical task in pursuing the optimal exploitation of forest resources. Production planning determines which logs are to be processed, taking into account their characteristics with the aim of satisfying the demand for final products. Logs are turned into lumber when they are cut according to a set of available cutting patterns (CPs). The development of efficient production planning is a key factor in improving the productivity of sawmills, and mathematical modeling is a suitable technique to achieve this objective. In this paper, a mixed integer linear programming (MILP) model for optimal daily production planning in sawmills is proposed. The model involves a set of CPs for each type of log, which is obtained through an exhaustive algorithm, attaining all possible feasible CPs. The proposed approach determines the optimal number of logs of each type to be cut, the selected CPs to be used, material inventory, demand fulfillment, and other industrial and commercial issues with the objective of maximizing the firm’s benefit, in reasonable computational time, considering the size of the problem.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".