A hybrid column-generation and genetic algorithm approach for solving large-scale multimission selective maintenance problems in serial <i>K</i>-out-of-<i>n</i>:<i>G</i> systems
Bibliographic record
Abstract
This paper introduces a solution method for the multimission selective maintenance problem (SMP) that combines column-generation (CG) and genetic algorithms (GAs). The multimission SMP is an optimisation problem that arises when a system performs a sequence of missions separated by breaks of finite duration. During these finite breaks, only a subset of possible maintenance actions can be performed due to resource limitations. The problem is in deciding what actions to perform during each break duration such that the system meets or exceeds a minimum target reliability for all missions. The resulting optimisation problems are usually modelled as mixed integer nonlinear mathematical programmes, which are hard to solve. They are usually solved using metaheuristics. We propose a solution method based on CG framework in which the subproblems are solved using a GA. By integrating the GA within the classical CG framework, high-quality solutions can be obtained very quickly. The proposed solution method is capable of solving systems composed of both parallel and k-out-of-n:G subsystems. This hybrid CG algorithm is shown to obtain near optimal solutions and outperform other metaheuristic solution methods; it is also shown to be capable of solving large-scale systems composed of many subsystems and hundreds of components in a reasonable amount of time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".