An Adaptive Annealing Genetic Algorithm for job-shop scheduling
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
Most production planning and scheduling applications are complex combination optimization in nature. Genetic Algorithm (GA), Simulated Annealing Algorithm (SAA) and Optimum Individual Protecting Algorithm (OIPA) have application limitations due to their performance in global convergence, population precocity and convergence speed, which make them not suitable for workshop daily operation planning applications. The Adaptive Annealing Genetic Algorithm (AAGA) studied in the paper has unique advantages to deal with the above limitations through 1) adaptively changing mutation probability to shorten the optimizing process and avoid the local optimization; and 2) integrating the Boltzmann probability selection mechanism from simulated annealing algorithm to select the crossover parents to avoid the population precocity and local convergence. The detail of AAGA is introduced and a typical application example for daily workshop operation scheduling is studied using GA, SAA, OIPA, and the proposed AAGA, respectively. As seen from the simulation results, the proposed AAGA shows an improved performance.
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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