Review of research on ship speed optimization driven by multi-source environmental information fusion

Ship speed optimization is a key technical measure for reducing energy consumption and emissions, and considering the dynamic navigation environment is crucial for achieving precise optimization. The research progress in the ship speed optimization field was systematically reviewed from three perspectives: environmental factor impact mechanisms, environmental information perception technologies, and optimization models and algorithms. The models have evolved from static planning to dynamic optimal control methods capable of handling time-varying environments. Meanwhile, data-driven techniques have been widely applied to environmental forecasting and energy consumption modeling, enhancing optimization accuracy and efficiency. Despite significant progress in ship speed optimization research, challenges remain in the refined modeling of ship-environment coupling mechanisms, the robustness and efficiency of optimization algorithms, and multi-objective collaborative optimization. Future work should strengthen research on coupling mechanisms, develop intelligent optimization algorithms that integrate machine learning with traditional methods, and expand the optimization scope from single ships to fleet collaboration to promote the continuous development and application of ship speed optimization technology.