Integrating Electric Energy Cost in Lumber Production Planning
Bibliographic record
Abstract
The arrival of digital technology in production systems represents a major challenge for manufacturers. The "4.0 Industrial Revolution" is pushing companies to review these same systems in order to develop decision-making tools that contribute to better capture any relevant opportunities while increasing profitability. In this context, this article shows a tactical planning model, specially developed for the lumber industry, integrating the electric energy cost in the decision process in order to minimize electric energy consumption. The model calculates the energy consumption based on equipment nominal power, the time at which the equipment is used, and a certain load factor. It also includes the energy used to heat or cool workspaces. Using real data from a North American sawmill collected from August 2017 to July 2018, the model showed that with a load factor calculated for each month and a good approximation of the heating energy consumed, the total energy consumption calculated is close to the one billed by the electricity supplier. Hence, the tactical planning tool could now be exploited by any sawmill aiming to integrate energy cost as a decision variable in its production planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".