Optimizing Markov decision process state design for deep reinforcement learning manufacturing scheduling using Bayesian optimization
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study investigates the application of Bayesian optimization for feature selection in Markov decision processes when applied to production scheduling problems. Traditional supervised learning feature selection methods are unsuitable due to the absence of explicit target values and the dynamic nature of scheduling environments. To address this, a bi-level optimization framework is proposed, with Bayesian optimization at the upper level for feature selection and reinforcement learning at the lower level for evaluation. Experimental results conducted in dynamic flexible job shop and thin-film transistor liquid-crystal display production scheduling environments demonstrate that the framework enhances efficiency by focusing on impactful features, reducing computational complexity, and improving decision-making. The findings highlight the significance of aligning state representations with scheduling dynamics and provide a foundation for future research on systematic feature selection in complex environments.
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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 | 0.001 | 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