Optimization of a bi-objective reliability redundancy allocation problem with heterogeneous components and strategy selection
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".