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.
