Improving the ADACOR2 supervisor holon scheduling mechanism with genetic algorithms
Bibliographic record
Abstract
Manufacturing companies are being pushed to their limits due to an increase of production complexity guided by a growing standards demand by the costumers. To respond properly to this, manufacturing companies must adopt innovative control architectures that are able to handle better the occurrence of disturbances at shop-floor level (e.g. workstation breakdown, orders cancellation or modification).Additionally, the selection of a proper scheduling algorithms assumes a crucial point, in the sense that the increase of optimization levels depend on this.This paper presents a Genetic Algorithm (GA) based technique to be embedded into the supervisor entity present at the ADACOR2 aiming to improve the existing fast and non-optimal scheduling technique, improving the overall system processing execution. The main requirements of the GA is to be fast enough to be usable in demanding environments improving the optimization output.The proposed algorithm is tested using a Flexible Manufacturing System using different configurations of transportation and batch sizes. Results show that despite the presented GA technique increased the optimization calculation time it performs better considering the sum of this time with the gain in the optimization output.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".