Production set of failure prone flexible manufacturing systems<sup>1</sup>
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
Abstract For a flexible manufacturing system, determining the feasible production set at a given time is essential for production management. In this paper, we propose methods for estimating the production set for a general multi–part type failure prone flexible manufacturing system. We also discuss how to get an open loop control that can ensure the system producing at the margin of its expected production set and how to estimate the corresponding variance of the expected production set. Some special flexible manufacturing systems are discussed where we also suggest some simpler approximations. Numerical examples are presented to illustrate our algorithm 1This research has been supported by NSERC–Canada, Grants # OGP003 6444 and FCAR NC0271 F 2E. K. Boukas and Q. Zhu are with the mechanical Engineering department, École Polytechnique de MontrÉal and the GERAD, Montréal, Que.,Canada. Bitnet Earn: Ar000Polytecl 1This research has been supported by NSERC–Canada, Grants # OGP003 6444 and FCAR NC0271 F 2E. K. Boukas and Q. Zhu are with the mechanical Engineering department, École Polytechnique de MontrÉal and the GERAD, Montréal, Que.,Canada. Bitnet Earn: Ar000Polytecl Notes 1This research has been supported by NSERC–Canada, Grants # OGP003 6444 and FCAR NC0271 F 2E. K. Boukas and Q. Zhu are with the mechanical Engineering department, École Polytechnique de MontrÉal and the GERAD, Montréal, Que.,Canada. Bitnet Earn: Ar000Polytecl
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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