Non-Linear Threshold Algorithm for the Redundancy Optimization of Multi-State Systems
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
To improve system performance, redundancy is widely used in different kinds of industrial applications such as power systems, aerospace, electronic, telecommunications and manufacturing systems. Designing high performant systems which meet customer requirements with a minimum cost is a challenging task in these industries. This paper develops an efficient approach for the redundancy optimization problem of series-parallel structures modeled as multi-state systems. To reach the target system availability, redundancies are used for components among a list of products available in the market. Each component is characterized by its own availability, cost and performance. The goal is to minimize the total cost under a system availability constraint. Discrete levels of performance are considered for the system and its components. The extreme values of such performance levels correspond to perfect functioning and complete failure. A piecewise cumulative load curve represents consumer demand. System availability corresponds to the aptitude to fulfill this demand. The multi-state system availability evaluation uses the universal moment generating function technique. The proposed optimization algorithm is based on the non-linear threshold accepting metaheuristic, while using a self-adjusting penalty guided strategy. The obtained results demonstrate the approach efficiency for solving the redundancy optimization problem of multi-state systems. Its effectiveness is also tested using the classical redundancy optimization problem of binary-state systems. The algorithm is evaluated by comparison to the best known methods. For multi-state systems, it is compared to genetic algorithm and tabu search. For binary-state systems, it is compared to genetic algorithm, tabu search, ant colony optimization and harmony search. The obtained results demonstrate that the proposed approach outperforms these state-of-the-art benchmark methods in finding, for all considered instances, a high-quality solution in a minimum computational time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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