关键词: 水路运输, 航道调度方案, 深度学习, 复合航道, 气象影响

Abstract: Meteorological conditions like wind, waves, and currents often reduce vessel speed in navigational channels, impacting transit times and carbon emissions. To optimize vessel sequencing and scheduling, this study investigates a multi-meteorological factor channel scheduling problem. A bi-objective mixed-integer programming model is developed to minimize carbon emissions and scheduling time deviations, incorporating constraints such as vessel speed loss, tidal time windows, channel capacity, and several transit rules. An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), enhanced with deep learning, is proposed to obtain Pareto-optimal solutions. The ML component combines k-means clustering and recurrent neural networks to estimate actual vessel speeds under varying weather conditions, while an adaptive parameter adjustment mechanism maintains population diversity in NSGA-II. Experimental results show that the proposed approach reduces speed estimation error from 11.2% to 1.4%. The Pareto front achieves two extremes: zero scheduling time deviation and a 60.8% reduction in carbon emissions. In terms of meteorological effects, the model effectively mitigates significant speed loss, improving the two objectives respectively by 11.8% and 67.0%. The sensitivity analysis indicates that vessel speed loss increases from 0.8 knots under mild conditions to 2.9 knots under moderate conditions, accompanied by rises of 90.4% in carbon emissions and 36.9% in time deviation. No feasible solution exists under severe conditions. These findings demonstrate the model's dual role: it provides trade-off solutions under most weather scenarios and works as an early warning system for extreme conditions.

Key words: water transportation, channel scheduling plan, deep learning, compound channel, meteorological influence