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首页> 外文期刊>Transportation research >Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data
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Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data

机译：使用航程报告数据的两阶段最优解决方案，用于航程中的船速和纵倾优化

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摘要

In the daily operations of a shipping line, minimization of a ship's bunker fuel consumption over a voyage comprising a series of waypoints by adjusting its sailing speeds and trim settings plays a critical role in ship voyage management. To quantify the synergetic influence of sailing speed, displacement, trim, and weather and sea conditions on ship fuel efficiency, we first develop a tailored method to build two artificial neural network models using ship voyage report data. We proceed to address the ship sailing speed and trim optimization problem by putting forward three viable countermeasures within an effective two-phase optimal solution framework: sailing speeds of the ship are optimized in an onshore planning phase, whereas trim optimization is conducted dynamically by the captain in real time when she/he observes the actual weather and sea conditions at sea. In the onshore speed optimization problem, simultaneous optimization of sailing speeds and trim settings is beneficial in suggesting more informed sailing speeds because both factors influence a ship's fuel efficiency. In the countermeasure 3 proposed by this study, we address speed and trim optimization simultaneously by proposing a two-step global optimization algorithm that combines dynamic programming and a state-of-the-art simulation-based optimization technique. Numerical experiments with two 9000-TEU (twenty-foot equivalent unit) containerships show that (a) the proposed countermeasure 1 saves 4.96% and 5.83% of bunker fuel for the two ships, respectively, compared to the real situation; (b) the proposed countermeasure 2 increases the bunker fuel savings to 7.63% and 7.57%, respectively; and (c) the bunker fuel savings with Countermeasure 3 attain 8.25% on average. These remarkable bunker fuel savings can also translate into significant mitigation of CO2 emissions. (C) 2019 Elsevier Ltd. All rights reserved.
机译：在航运公司的日常运营中，通过调整航行速度和纵倾设置，使包括一系列航路点的航行中的船用燃油消耗最小化，在船舶航行管理中起着至关重要的作用。为了量化航行速度，排水量，纵倾以及天气和海况对船舶燃料效率的协同影响，我们首先开发了一种量身定制的方法，以使用船舶航行报告数据构建两个人工神经网络模型。我们通过在有效的两阶段最佳解决方案框架内提出三种可行的对策来解决船舶航行速度和船体优化的问题：在陆上计划阶段优化船舶的航行速度，而船长动态地进行船体优化当她/他观察海上的实际天气和海况时实时进行。在陆上速度优化问题中，同时优化航行速度和纵倾设置有利于建议更明智的航行速度，因为这两个因素都会影响船舶的燃油效率。在这项研究提出的对策3中，我们通过提出一种两步全局优化算法来同时解决速度和修整优化问题，该算法将动态规划和基于最新仿真的优化技术相结合。用两艘9000 TEU（二十英尺当量单位）的集装箱船进行的数值试验表明：（a）拟议的对策1与实际情况相比，两艘船分别节省了4.96％和5.83％的船用燃料； （b）拟议的对策2将船用燃油节省分别提高至7.63％和7.57％； （c）采用对策3的燃油节省平均达到8.25％。这些显着的燃油节省还可以转化为二氧化碳排放量的显着减少。 （C）2019 Elsevier Ltd.保留所有权利。

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