Selective maintenance scheduling over a finite planning horizon
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
A preventive maintenance scheduling model is proposed in this article. The proposed model includes finite planning horizon and limited available resources to perform maintenance scheduling. A subset of maintenance actions, that is, selective maintenance is needed during maintenance breaks due to limited resources such as time, cost, and repairman availability. Maintenance can not only improve the effective age of a component but also may alter the hazard rate. Therefore, a hybrid imperfect maintenance model is used in this article that considers the combined effect of age reduction and hazard adjustment on a component. For a multi-component system, selective maintenance is performed at periodic intervals. In addition to maintenance and failure costs, we have included the maintenance break duration and the shutdown cost in the proposed scheduling model. A periodic maintenance scheduling problem is solved in this article for a series–parallel system. The optimal number of periodic maintenance breaks in a finite planning horizon is determined. Also, maintenance actions required during each of the maintenance breaks are determined. The number of periodic maintenance breaks and maintenance actions during these breaks is selected in a way that the total maintenance, failure, and shutdown cost are minimum. An evolutionary algorithm is used to solve the problem.
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 | 0.002 | 0.004 |
| 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