Availability modeling and optimization of reconfigurable manufacturing systems
Bibliographic record
Abstract
Reconfiguration mechanisms lead to the design of robust manufacturing systems which have the capability to allow the service continuity, in the presence of a failure, on the basis of a minimal degradation of performances. In this paper, a stochastic model is proposed to assess and to analyze the availability of reconfigurable systems whose equipments are subject to random failures. To distinguish between the normal behavior and the degraded one, the production rate is used as a performance measure. An availability model that takes into account the performance degradation is developed. Close form solutions of the steady‐state availability and the production rate of a reconfigurable system are calculated. Two optimization problems dealing with reconfigurable systems are also addressed. The paper considers a series system consisting of N stochastically independent components. Different technologies are assumed to be available for each component. The following design problems are studied: find the configuration, which allows maximizing the production rate of the system under resource constraints; and find the configuration that allows to reach some predetermined level of production rate at minimal cost. The design model of the first problem leads to mixed linear programming, while the design model of the second problem leads to integer linear programming. A numerical procedure is developed to solve both problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".