Optimal production scheduling for manufacturing systems with preventive maintenance in an uncertain environment
Why this work is in the frame
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Bibliographic record
Abstract
Consider a manufacturing system in which a single consumable good is fabricated in a process that consists of k stages in an uncertain environment. On each stage, there are a number of workstations that are assumed to have different operating parameters that are subject to failure, repair, and preventive maintenance which generate discrete jumps in the value of the state. A just-in-time manufacturing discipline is assumed for the workstations with running costs that include penalties for shortfall and surplus production. The formulation presented for the optimal production scheduling for the manufacturing system requires extensions to the results of the LQGP problem with state dependent Poisson processes (SDPP) by the inclusion of coefficients for the dynamics and the costs that are parameterized by the value of the state. The cost functional used is fully quadratic which is an enhancement for the LQGP problem. The functionality of this canonical model is demonstrated with a numerical example.
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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