Makespan minimization for parallel machine scheduling of semi-resumable and non-resumable jobs with multiple availability constraints
Why this work is in the frame
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Bibliographic record
Abstract
The problem of makespan minimization for parallel machine scheduling with multiple non-availability periods for non-resumable, semi-resumable and resumable jobs is considered in this paper. The discussion of this problem is very limited in the current state of the literature, with most cases only considering a small subset of the problem such as a single machine with multiple non-availability periods or several machines with only one non-availability period. The problem is formulated as a mixed-integer linear programming model and solved optimally using CPLEX for small problems with multiple availability constraints on all machines. Several heuristic approaches are then applied to the problem to provide near-optimal solutions while significantly reducing the computation time. Numerical results are obtained for several experiments and the performance of the heuristic methods is analysed.
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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 | 0.001 | 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 | 0.001 |
| 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