Scheduling identical parallel machine with unequal job release time to minimise total flow time
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the identical parallel machine scheduling problem with unequal jobs release date to minimise the total flow time. An efficient heuristic algorithm was proposed, known as modified forward heuristic algorithm. The algorithm starts with developing a priority list of all jobs. This list is used to develop sub-schedules for each machine based on some propositions related to the jobs processing and release times with allowing delay schedule. A mathematical model of the problem was also developed. The performance of the algorithm was evaluated by comparing its solutions with the optimal solutions of small test cases obtained from the developed mathematical model. Then, the results of large problems were compared with the results of the best reported heuristics in the literature. In addition to the simplicity of the proposed algorithm, these comparisons showed that the proposed algorithm can obtain solutions that are very close to the optimum solutions and better than the other heuristics.
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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