Physical programming and conjoint analysis-based redundancy allocation in multistate systems: A Taguchi embedded algorithm selection and control (TAS&C) approach
Bibliographic record
Abstract
Amidst increasing system complexity and technological advancements, the manufacturer aims to win the consumer's trust to maintain his or her permanent goodwill. This expectation directs the manufacturer to address the problem of attaining desired quality and reliability standards; hence, the measure of performance of a system in terms of reliability and utility optimization poses an issue of primary concern. In order to meet the requirement of a reliable and trouble-free product, optimal allocation of all conflicting parameters is essential during the design phase of a system. With this in mind, this paper presents a physical programming and conjoint analysis-based redundancy allocation model (PPCA-RAM) for a multistate series—parallel system. Use of physical programming approach is the key feature of the proposed algorithm to eliminate the need for multi-objective optimization. Physical programming methodology provides an adequate balance among various associated performance measures and thus provides an efficient tool for formulating the objective function of a practical redundancy allocation problem. The proposed model has been addressed by a novel methodology called Taguchi embedded algorithm selection and control (TAS&C). An illustrative example has been presented to authenticate the efficiency of the proposed model and algorithm. The results obtained are compared with the genetic algorithm (GA), artificial immune system (AIS), and particle swarm optimization (PSO), where TAS&C was seen to significantly outperform the rest.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".