Dynamic Flexible Job Shop Scheduling via an Adaptive Genetic Algorithm and Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
To address the scheduling problem of dynamic flexible job shop, this study proposes a hybrid scheduling method that integrates an adaptive genetic algorithm, dynamic target smoothing, and a deep Q-network (DQN). The scheduling process is formulated as a Markov decision process, where a graph convolutional network (GCN) extracts feature representations from evolving job and machine states. The adaptive genetic algorithm dynamically generates target values, while the dynamic target smoothing mechanism—based on sliding windows or exponential smoothing—further stabilizes target updates and enhances training efficiency. Experiments on the Brandimarte benchmark with stochastic job arrivals show that the proposed method reduces makespan by up to 2.1% compared to the QNGA baseline. In addition, the integration of adaptive evolution and smoothed target learning provides more stable training and stronger adaptability to dynamic environments than the existing DQN-based approaches.
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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 | 0.001 |
| 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