Integrating Electric Energy Cost in Lumber Production Planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it