Periodic replacement strategies: optimality conditions and numerical performance comparisons
Bibliographic record
Abstract
In maintenance engineering, age replacement policy (ARP) and block replacement policy (BRP) are the most popular basic strategies. They have been intensively studied and compared using different performance measures. Several of these comparisons are stochastics on the basis of the renewal theory, and a few of them are of economic benefit. This paper presents a comparative study for analysing ARP and BRP models using the expected costs function as the principal criterion. To provide this comparison, we propose a numerical approach allowing to combine cost/distribution for the determination of the optimal strategy. For that, we resume the main analytical results and prove that a finite solution exists if the failure rate increases. Results clearly show that both strategies are very close, which intuitively confirm the statement of Barlow and Proschan’s theorem. Based on the computational results, we show that the ultimate decision to select the best strategy is conditioned by the choice of the distribution function, the value of its parameters and that the periodic replacement unit cost must be much lower than the replacement unit cost at failure.
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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 | 0.004 | 0.018 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".