Abstract: Additional resistance induced by shallow water effects is a critical factor influencing fuel economy during ship design and operation, especially in shallow sea areas such as the Baltic Sea. To address fuel efficiency challenges in such regions, this paper proposes a speed optimization model that accounts for shallow water resistance. The model adopts a multi-objective genetic algorithm integrated with a hypervolume indicator to optimize both total fuel consumption and voyage time. Three operational strategies are investigated: minimizing overall fuel consumption, applying shallow water resistance constraints, and maintaining constant optimal engine power. The effectiveness of each strategy is assessed on a typical Baltic route. Results show that the proposed optimization strategy can reduce fuel consumption by approximately 3.6% without compromising voyage schedules. Moreover, the additional fuel consumption attributed to shallow water effects can be limited to 2.5%. The proposed approach demonstrates strong effectiveness and engineering applicability, providing theoretical support and optimization guidance for green ship design and intelligent speed control.

Key words: ship speed optimization, shallow water effects, genetic algorithm, green shipping

摘要： 在船舶设计与营运阶段,浅水效应带来的额外阻力是影响燃油经济性的重要因素,在波罗的海等浅水海域尤为明显。该文针对浅水区域燃油优化问题,提出一种融合超体积指标的多目标遗传算法,构建了以优化总燃油消耗量与航行时间为双目标的航速优化模型,并将浅水效应引起的阻力变化纳入建模考量;进而,对比分析了不同的优化策略（包括总耗油最小化、浅水影响约束优化及恒定最优功率运行）在典型航线上的应用效果。结果表明,优化后的营运策略可在保证航期的前提下节省约3.6%的燃油,浅水效应所导致的额外燃油消耗占比可控制在2.5%以内。研究结果验证了文中所提算法的有效性和工程适用性,可为绿色船舶设计与智能航速控制提供理论依据与优化指导。

关键词: 航速优化, 浅水效应, 遗传算法, 绿色航运