A critical review of selective maintenance for mission-oriented systems: challenges and a roadmap for novel contributions
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".