Tactical-operational coordination of a divergent production system with coproduction: the sawmilling challenge
Bibliographic record
Abstract
Various optimization tools have been used in industry to facilitate production planning at different levels of aggregation. Choosing the interoperability mechanisms of these systems, such as the planning frequencies, the information passed between them and the interpretation that other systems must make of them, has always been a challenge. This work focusses on production planning at the tactical and operational levels in North American sawmills, a commodity industry characterized by volatile prices and a divergent production process with coproduction. In this context, tactical planning produces aggregated plans, and information from these plans can be used as targets and/or constraints at the operational level (e.g., quantities to be produced/kept in stock per product and per period, sales targets, etc.). A simulation of this production system was therefore developed, encompassing the planning process and the market dynamic, to compare and evaluate the impact of different coordination approaches on business economic performance. Results showed that the type of information which should be shared from the tactical level to the operational level varies according to several factors, including the company’s order acceptance policy, price seasonality, and the presence or absence of overcapacity on the market.HighlightsA simulation approach is used to evaluate coordination between tactical and operational planningThe context of North American sawmills is the one investigatedThe production and the planning process as well as the market behavior is consideredResults show that the information shared between the two levels impact the incomeThe order acceptance policy chosen also has an influence on the revenue generated
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".