Failure-prone manufacturing systems with setups: feasibility and optimality under various hypotheses about perturbations and setup interplay
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
A failure-prone manufacturing system producing two part types and requiring a setup for switching from one part type to another is considered. Both setup cost and time are taken into account. Various hypotheses regarding the interactions between random perturbations (failures and repairs) and setup strategies are used in the scientific literature, without clarifications provided for their relationships and possible consequences. In this paper, we close this gap and address feasibility and optimality conditions under various hypotheses. The feasibility conditions are obtained and studied analytically, and are shown to be dependent on the choice of an adopted hypothesis. This finding will prevent feasibility conditions not matching the underlying hypothesis from being applied. Optimality conditions in the form of Hamilton-Jacobi-Bellman equations are obtained and shown to be also dependent on the adopted hypothesis. A numerical example illustrating a comparison of the results obtained with the solutions of HJB equations is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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