A Decision-making Framework for Repair vs Replacement of a Multi-Component System Subject to Environmental Shocks
Bibliographic record
Abstract
SUMMARYThis paper considers a repairable system, which can be either repaired or replaced when a failure occurs, and earns income as a function of its operation time. The main aim of the current paper is to develop a dynamic decision-making framework to decide whether a failed system has to be repaired or replaced by a new one. The decision is made based on the mean profit of the repair and replacement scenarios. The most profitable scenario would be the output of the framework. The dynamicity of the strategy backs to the fact that it can alternate between repair and replacement depending on the system’s state. The framework is also dynamic in the sense of time because the system’s income, and the repair and penalty costs are functions of time. The mean profit is constructed based on the system’s income, the repair and purchase cost, the imposed costs regarding the system's shut downtime and repair time, and the waiting time for the delivery of a new system. The cost factors are considered functions of time and the repairs are assumed to be imperfect. To apply the proposed framework when the system breaks down, the responsible engineer needs two inputs to make the decision: the meantime to repair and the degree of repair by the repairman. Given the inputs, if the benefit of replacement is more than the benefit of repair, the system should be replaced; otherwise, the system should be repaired. Some numerical results and a real-world example are presented to assess the behavior and illustrate the application of the proposed framework.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".