Optimizing a joint reliability-redundancy allocation problem with common cause multi-state failures using immune algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Redundancy-reliability allocation problem (RRAP) is a well-known problem in reliability area. In general, this problem aims to maximize a system’s reliability or minimize a system’s costs under some constraints. In this paper, we develop a RRAP for a series-parallel system with multi-state components. Thus, the subsystems’ components, the system’s subsystems, and the system have different working states with corresponding working probabilities. The RAP in the paper is a RAP with mix components (RAPMC). We consider the choice of allocating non-identical components to each sub-system. Moreover, we consider the common cause failure (CCF) for the components, which causes simultaneous failure of all identical components of a subsystem. We assume the component’s failure state probability is reduced by conducting technical activities, and the reduced probability is added to the component’s working states’ probabilities. The model’s objective function is to minimize the system’s costs under a minimum reliability level and other constraints by allocating the optimal set of components to each subsystem and determining each component’s technical activities level. Since the RRAP belongs to the Np-Hard category of problems, an immune algorithm is used to solve the developed problem. The results indicate considering the technical activities decreases the system’s costs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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