Optimization of Ship Routing and Energy Efficiency
Summary
The shipping industry accounts for a significant share of global energy use and greenhouse gas emissions, driving concerted efforts to refine voyage planning and operational strategies. Optimisation of ship routing integrates meteorological forecasts, ocean currents and vessel performance characteristics to reduce fuel consumption while preserving safety and schedule integrity. Techniques range from classical route-planning algorithms through weather routing to dynamic adjustment of speed, trim and ballast conditions. Slow steaming, the practice of deliberately reducing cruising speed below design parameters, has emerged as a powerful lever for lowering bunker fuel use and CO₂ output, albeit with trade-offs in transit time. Advanced decision-support systems now combine semi-empirical resistance models, real-time environmental data and machine learning to forecast ship behaviour under varying conditions. This multidisciplinary approach harnesses big data, neural networks and data fusion to deliver precise predictions of fuel burn, enabling voyage planners and ship masters to select optimal paths and speed profiles. Collectively, these developments enhance economic performance, help shipping comply with increasingly stringent regulatory frameworks and contribute to the decarbonisation of maritime transport.
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Optimization of Ship Routing and Energy Efficiency publication trend
The graph below shows the total number of articles in optimization of ship routing and energy efficiency across all publications each year (not limited to Nature Index journals).
Technical terms
Weather routing: Use of meteorological and oceanographic data to plan energy-efficient vessel pathways.
Slow steaming: Intentional reduction of cruising speed below design speed to minimise fuel consumption and emissions.
Speed through water (STW): Vessel velocity relative to surrounding water, excluding the influence of currents.
Speed over ground (SOG): Actual ship velocity relative to the Earth’s surface, including the effects of currents.
Artificial neural network (ANN): A machine learning architecture inspired by biological neurons, utilised to model and predict ship performance metrics such as fuel burn.
References
- A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping. Ocean Engineering (2015).
- Data fusion and machine learning for ship fuel efficiency modeling: Part I – Voyage report data and meteorological data. Communications in Transportation Research (2022).
- Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data. Journal of Marine Science and Engineering (2021).
- Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping. Sustainability (2020).
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