An Energy-Aware Flexible Job-Shop Scheduling Problem with Sequencing Flexibility and Sequence-Dependent Setup Times
Why this work is in the frame
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Bibliographic record
Abstract
The manufacturing panorama is rapidly changing to adjust to satisfy customer demands while addressing concerns over environmental impacts. Hence, this paper proposes an extension of the Flexible Job-Shop Scheduling problem. The extension allows arbitrary precedence relationships between the operations given by a directed acyclic graph. Additionally, it considers setup times that are dependent on the sequence of the jobs as well as the total energy consumption comprised of processing and idle energy. Hence, the problem consists of allocating operations to machines and sequencing them according to precedence relationships and dependent setup times. A multi-objective mixed-integer linear programming model is formulated with the objectives of makespan and total energy consumption minimization. The formulation is solved with the weighted-sum approach. A numerical experiment is performed on five small randomly generated instances to show the model’s applicability. The model allows the incorporation of real-world settings that have been studied independently.
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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