Optimizing a joint reliability-redundancy allocation problem with common cause multi-state failures using immune algorithm
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".