Industrial Application of a Continuous-Time Scheduling Framework for Process Analysis and Improvement
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
We investigate the potential benefits of applying short-term continuous-time scheduling optimization to an industrial multiproduct food processing plant. The facility exhibits several unique production policies that are not supported by current continuous-time scheduling models. Several extensions to a general global event continuous-time scheduling model are proposed to address these limitations. In addition, problem knowledge is exploited to develop an aggregate reformulation of the model for application to the target problem. The potential benefits of scheduling optimization are assessed through several case studies. Historical plant performance is benchmarked, and the possible gains in production efficiency are highlighted. In addition, the scheduling models are used to evaluate potential benefits of operating policy and plant configuration changes.
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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 | 0.001 |
| 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 | 0.001 |
| 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