A genetic algorithm for one-job m-machine flowshop lot streaming with variable sublots
Why this work is in the frame
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Bibliographic record
Abstract
Lot streaming is a technique used to split the processing of lots (batches) into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production makespan. In this technique, a production lot may be split into equal, consistent or variable sublots. Recent literature shows that, when production setup time is considered, significant lead time improvement is possible if variable sublots are used. In this research, however, we noted that lot streaming problems with variable sublots are difficult to solve using off-shelf optimisation packages even for problems of smaller sizes. Thus, efficient solution procedures are needed for solving such problems. In this paper, we develop a hybrid genetic algorithm for a model that appeared in recent literature for one -job m -machine lot streaming problems with variable sublots and setup. Computational results showed that the performance of the proposed genetic algorithm is encouraging.
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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 | 0.000 |
| 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