Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data

Yuquan Du, Qiang Meng, Shuaian Wang and Haibo Kuang

Transportation Research Part B: Methodological, 2019, vol. 122, issue C, 88-114

Abstract: 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 on-shore 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 on-shore 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.

Keywords: Ship fuel efficiency; Speed optimization; Trim optimization; Neural network; Data-driven optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (24)

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DOI: 10.1016/j.trb.2019.02.004

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