Based on AIS data ship path optimization algorithm considering wind speed and ocean current environmental factors to reduce carbon emissions

Maritime transportation plays a pivotal role in global trade, yet it contributes significantly to carbon emissions due to inefficient routing. This paper proposes a genetic algorithm (GA)-based ship path optimization framework that leverages Automatic Identification System (AIS) data to incorporate environmental factors such as wind speed and ocean currents, aiming to minimize fuel consumption and carbon emissions. The methodology integrates a graph-based representation of maritime grids, where nodes account for dynamic environmental penalties, and GA evolves optimal paths by balancing distance, fuel efficiency, and emission reduction. Experimental evaluations on real AIS datasets demonstrate that the proposed algorithm achieves a 15% reduction in fuel consumption compared to traditional baselines like Dijkstra and A*algorithms, while maintaining comparable travel times. Key innovations include an adaptive mutation rate and elite preser-vation strategy, ensuring convergence within computational constraints. The results highlight the potential for sustainable shipping practices, with implications for reducing the maritime sector’s environmental footprint. Sensitivity analyses further validate the model’s robustness across varying wind scales, underscoring its practicality for real-world deployment. This work advances data-driven optimization in maritime logistics, promoting greener and more efficient global supply chains.