Selective maintenance optimization for 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
In many military and industrial environments, systems are required to perform a sequence of missions with finite breaks between missions. During the breaks, it may be impossible to perform all desirable maintenance activities prior to the beginning of the next mission. What maintenance activities should be performed during the limited amount of time is a problem deserving studies. This kind of problem is called a selective maintenance problem. In this paper, we consider a parallel-series system with M subsystems connected in series wherein each subsystem consists of N/sub i/ identical components connected in parallel. Each component and the system may be in K+1 possible states, i.e., 0, 1, 2, a, K. When the system comes into the maintenance depot from the previous mission, the states of the components and the system can be determined. An optimization model is presented for minimization of total maintenance cost subject to system state probability requirements for the next mission. The shortest path method is used to solve the integer nonlinear programming problem.
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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