MAKESPAN MINIMIZATION FOR PARALLEL MACHINES SCHEDULING WITH AVAILABILITY CONSTRAINTS
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
A new method is developed to schedule jobs on parallel machines with availability constraints. The objective of the problem is to minimize the makespan of the total production schedule. Without the availability constraints the scheduling of machines is a Pm || Cmax problem. The scheduling of this problem was the topic of many earlier papers.\nThe main contribution of this research is that the schedule of the jobs on parallel machines with availability constraints is determined within a single implicit enumer- ation algorithm. Within the general enumeration scheme, the loads of each machine are enumerated in a lexicographic order. An exact integer linear programming model is provided, too. The difficulty of the problem depends on the properties of the pro- cessing times, the number of machines, and the number of availability constraints on the machines. In some subclasses, problems with very large number of jobs are solved. The largest problems solved within one hour limit have 1, 000, 000 jobs.
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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 | 0.001 | 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