To maintain or not to maintain? What should a risk‐averse decision maker do?
Bibliographic record
Abstract
Purpose In real‐life applications maintenance managers often face complicated decision problems under uncertainty. This difficulty increases when they have to take conflicting objectives into account. A recent review of the literature shows that previous works consider repairable systems subject to random failures and analyse trade‐offs between the costs and the benefits of maintenance activities. The risk aversion of the maintenance decision maker may be not underlined enough. This paper aims to deal with a single component system that has to accomplish a series of missions of a given length. Design/methodology/approach The development of a maintenance strategy for this system is analysed from a risk aversion point of view. An attempt is made to highlight the attitude of a neutral decision maker versus a risk‐averse manager. Findings Presents a very simple framework to analyse the risk aversion effect on managers' decisions. The model confirms the observation that risk aversion implies no‐monotone relation between optimality frequencies of maintenance operations and the deformation rate of the breakdown probability. Originality/value Since the deformation rate is monotonic with time, the proposed model can be extended to derive optimal frequencies, which allow the implementation of the optimal deformation rates according to the probability law of the deformation rate δ.
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.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".