Optimization of a bi-objective reliability redundancy allocation problem with heterogeneous components and strategy selection
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
As a challenging problem in reliability area, researchers have focused on reliability-redundancy allocation problem (RRAP) to design high reliable systems. Almost all the studies on the RRAP are restricted by two assumptions, using identical components and employing a predetermined redundancy strategy for subsystems. Besides, designing systems with high reliability needs a considerable number of resources, especially financial ones. Consequently, it is crucial to consider the reliability and cost of the system as two conflicting objectives for RRAP. The current study introduces a new mathematical model for a bi-objective RRAP, where the redundancy strategy in each subsystem is considered as a decision variable. Therefore, the mathematical model can select the best strategy for each subsystem from among the three active, standby, or mixed ones. To make the model more realistic, using heterogeneous components in each subsystem is also considered. To have an exact evaluation of the reliability in subsystems with standby and mixed strategies, a Markov-based approach is developed. Finally, an NSGA-II algorithm is utilized to solve the proposed model. The results reveal the superior performance of the proposed model.
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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