Optimimization of group scheduling using simulation with the meta-heuristic Extended Great Deluge (EGD) approach
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
Many companies apply cellular manufacturing systems (CMS) in order to improve production. One of the most significant problems encountered in production management is the scheduling problem, which has also been proven to be NP-hard. The objectives of the group scheduling problem in manufacturing are considered in order to minimize the makespan, the total flowtime and machine idletime. In this paper, we propose an approach for optimizing the scheduling of the manufacturing tasks of all parts of a product family, including exceptional elements. To solve this problem, an Extended Great Deluge (EGD) approach algorithm is applied in order to determine the optimal sequence of parts in each cell, minimizing the makespan and the total flowtime; following that, a heuristic method is applied to introduce exceptional elements. The results of the proposed hybrid approach show a major improvement when compared with those obtained using one of the best algorithms that has so far been presented by other researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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