Application of Model Predictive Control to Control Transient Behavior in Stochastic Manufacturing System Models
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Bibliographic record
Abstract
Increasing complexity in manufacturing strategies and swift changes in market and consumer requirements have driven recent studies of manufacturing systems, with transient behavior being identified as a key research area. Till date, satisfying consumer demand has focused on steady-state planning of production, mostly using stochastic or deterministic optimal control methods. Due to the difficulty of obtaining optimal control for many practical situations, as well as in evaluating performance under optimal control, these studies have not been conducive to the analysis or control of transient behavior. This paper bridges this gap by applying model predictive control to a manufacturing system modeled as a discrete-time Markov chain. By modifying the initiation of production as probabilities within the Markov chain, a method is proposed to directly control the system to specific expected performance levels and improve its stochastic transient behavior.
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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 | 0.001 |
| 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