Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis
Bibliographic record
Abstract
A mixed integer programming model that aims at supporting the tactical wood procurement decisions of a multifacility company is presented. This model allows for wood exchanges between companies. Furthermore, the material flow through the supply chain is driven by both a demand to satisfy ("pull" strategy) and a market mechanism ("push" strategy), enabling the planner to take into consideration both wood freshness and the notion of quality linked to the age of harvested wood into log, chips, and end-product demands. An inability to consider alternative plans for implementation, and the difficulty of assessing the performance of these plans in an uncertain environment, are two shortcomings of the manual planning process. A planning process, based on human planner – decision support system interactions that allows a company to overcome these shortcomings is therefore presented. The process combines Monte Carlo methods and an anticipation mechanism that will, in the long term, enable the company to take into account equipment transportation costs. The proposed planning process leads to a multicriteria decision-making problem where the human planner has to select a plan to implement from a set of candidate plans. A hypothetical test case shows that it is possible to manage the wood flow from stump to end market in such a way as to preserve freshness and extract higher value from the logs processed in the mills. The test case also shows that the proposed planning process achieves an average profitability increase of 8.8% compared with an approach based on a deterministic model using average parameter values. Finally, a sensitivity analysis reveals that the accuracy of standing inventory on harvest blocks and the anticipated market conditions are the most important parameters to consider in selecting a good wood procurement plan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".