The distributed flow shop problem under carbon quota constraints
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Bibliographic record
Abstract
This study delves into the Distributed Flowshop Scheduling Problem (DFSP) and introduces an innovative Iterative Greedy (IG) algorithm to address the gap in existing research regarding carbon quota constraints. By integrating a carbon quota limit into our model, we aim to minimize the makespan without exceeding predefined energy consumption thresholds. Initially, we employ an enhanced NEH algorithm to produce superior initial solutions, thereby boosting the algorithm's efficiency. Subsequently, we refine the solution's destruction and reconstruction process during the construction phase and introduce a dynamic speed adjustment strategy that adheres to energy consumption limits. This approach not only satisfies carbon quota constraints but also significantly reduces the makespan. Through meticulous parameter calibration and extensive performance evaluations across 720 instances, we validate the efficacy of our proposed IG algorithm in tackling such complex scheduling challenges.
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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 | 0.000 |
| 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