Selective preventive maintenance scheduling under imperfect repair
Bibliographic record
Abstract
The demand for a system is sometimes available only for a finite time horizon. It thus becomes necessary to schedule maintenance activities during a given planning horizon such that the desired system performance is maintained and the available resources are optimally allocated. In this paper, a mathematical model is proposed for periodically planning preventive maintenance activities for a system comprising multiple components. Due to resource limitations, it may not be possible to perform all desired maintenance options; hence, a selective maintenance approach is used to find the components to be maintained and maintenance actions to be performed on the selected components. An imperfect maintenance based hybrid model is considered here which includes age reduction as well as hazard adjustment after maintenance. Due to the high dimension of the solution domain, evolutionary approach is used to solve the problem. The optimal number of intervals is found under reliability and maintenance time constraints. During each maintenance break, the optimal maintenance option is selected for each component such that the overall cost of maintenance and possible failures for the entire planning horizon is minimized. It is also found that considering one interval at a time will incur higher cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".