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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.017 | 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 teacher head, 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".