A critical review of selective maintenance for mission-oriented systems: challenges and a roadmap for novel contributions
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
The selective maintenance problem (SMP) arises in many mission-oriented multi-component systems that are operated for consecutive missions interspersed with finite breaks, during which only limited component repairs can be performed due to constrained resources. This NP-hard problem decides which components to maintain and to what levels of repair to guarantee a pre-specified performance level during the subsequent mission. Over the last two decades, a sizeable body of literature has been published on this topic. However, the contributions have stagnated in quality, and most articles deal with small to moderate problems. This paper provides a critical review of the SMP literature. A total of 136 research articles related to SMP are reviewed and a selection of key representative models is discussed in detail. This review is framed according to two feature categories: formulation characteristics, composed of three sub-groups of characteristics related to the system, maintenance and mathematical model characteristics; and solution approaches, grouped by exact methods and approximate algorithms. This critical review is aimed at identifying drawbacks, shortcomings, and blind spots of the SMP literature, and providing a roadmap for the challenges to be addressed and innovative future research topics to further advance the academic and industrial contributions of SMP.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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