Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship’s fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship.
When sailing on the open seas, far from onshore dockyards, if a crucial part of the ship's machinery fails, the ship will experience a costly event that carries a high risk of seriously affecting ship operations. If the ship receives warning of an impending defect, then it can try to sail to a dockyard and simultaneously order the spare parts needed to fix the problem. In this paper, we define this type of maintenance situation as 'vessel emergency maintenance'. It is a complex problem, due to uncertainties with both the machinery condition development and spare parts delivery. To solve this problem, our paper proposes a bi-objective model under a condition-based maintenance strategy, with the aim of simultaneously minimizing maintenance costs and maximizing ship reliability. Maintenance costs include four things: (1) fuel consumption costs; (2) renting extra vessels; (3) shipping delay penalty costs; and (4) spare parts inventory costs. Ship reliability is represented by the reliability of the ship's main engine, and can be described through a stochastic process. To solve this bi-objective model, we employ a non-dominated sorting genetic algorithm II (NSGA-II) to generate the Pareto optimal front of the two objectives. A numerical experiment is presented to demonstrate the applicability of the proposed model. The results indicate that the proposed model can provide emergency maintenance decision support for ship operators while they are sailing at sea.
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