Review on ship onboard machinery maintenance strategy selection usingmulti-criteria optimization
Bibliographic record
Abstract
Marine shipping is an important aspect of the transportation system in Canada.It is estimated that 70-80% of items that we are surrounded by and use daily are brought by ships.Canadian businesses need to sell to the world and ships carries their products abroad.For people that live in Canada's island or northern communities, marine shipping is often the only source they have for essentials.It is estimated that marine shipping directly contributes about $3 billion annually to Canada's GDP through employment and other impacts.In a marine ship system, safety and reliability are very important considerations.The various system elements must be properly maintained and organizations are now looking to maintenance optimization to achieve optimum safety, machinery reliability and reduced costs.Modern day maintenance optimization is a decision-making problem which need to satisfy multiple and conflicting criteria.Multi-Criteria Optimization (MCO) techniques have been used in maintenance optimization.Two main classes of maintenance MCO problems have been identified as strategy selection and interval optimization.In marine ships, maintenance strategy selection is a complex decision-making problem that has become ever more challenging to address and is accompanied by diverse constraints and economic considerations.Each maintenance strategy has its own characteristics, importance and drawbacks.The use of inappropriate maintenance strategy affects the safety of a ship, crew, machinery reliability, maintenance cost etc.MCO techniques have been used in selecting optimal maintenance strategy for ship onboard machinery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".