Comparison of Deterministic and Stochastic Production Planning Approaches in Sawmills by Integrating Design of Experiments and Monte-
Bibliographic record
Abstract
iii Forest industry is one of the key economic initiatives in Quebec (Canada). This industry has recently faced some obstacles, such as the scarcity of raw material, higher competitiveness in the market and new obligations applied in North America regarding sustainable development. These problems force lumber industries to improve their efficiency and become more service sensitive, in order to ensure the on time demand fulfillment. To achieve the goals aforesaid, one solution is to integrate the uncertainties more appropriately into production planning models. Traditional production planning approaches are based on deterministic models which in fact, ignore the uncertainties. A stochastic production planning approach is an alternative which models the uncertainties as different scenarios. Our goal is to compare the effectiveness of deterministic and stochastic approaches in sawing unit of sawmills on a rolling planning horizon. The comparison is performed under different circumstances in terms of length of planning horizon, re-planning frequency, and demand characteristics defined by its average and standard deviation. The design of experiments method is used as a basis for performing the comparison and the experiments are
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".