Multi-Objective Ship Voyage Optimization Framework for Underwater Noise Emission
Bibliographic record
Abstract
Abstract Anthropogenic noise from marine shipping and other sources poses a serious threat to marine mammals and the ocean ecosystem. This paper aims to enhance a ship’s adaptability to varying ocean conditions, with the primary objective of reducing its contribution to underwater radiated noise (URN) along with fuel consumption. A new multi-objective optimization framework (MOOF) is developed that optimizes the ship’s sailing speed using a non-dominated sorting genetic algorithm (NSGA-II) to mitigate URN. Two objective functions: i) total noise intensity levels and ii) total fuel consumption are minimized under some voyage constraints. Subsequently, the Pareto solutions obtained from NSGA-II are further processed using a Euclidean-based multiple-criteria decision-making method to find the trade-off solution. To illustrate the efficacy of the MOOF, we consider a practical case study of a 6900 TEU containership in a voyage scenario. The proposed framework has shown a 94% reduction in the total intensity of URN, with a notable drop of 1.22 dB. Nevertheless, this accomplishment is accompanied by a slight increase in fuel consumption of 3.25 MT, or 0.4%. Thus, in the case of a realistic shipping route, MOOF quantitatively demonstrates its efficacy in significantly reducing URN intensity levels without enforcing a major compromise on fuel consumption. Further discussion on the optimized speed profiles and prospects is presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".