Dynamic Continuous Berth Scheduling Under Tidal Constraints and Carbon Costs Using QER-DDQN

Under the background of green and low-carbon port development, the single-terminal continuous berth dynamic scheduling problem is simultaneously affected by multiple factors, including dynamic vessel arrivals, tidal conditions, quay crane resources, and carbon emission costs, and has become a complex decision-making problem that must balance operational efficiency and low-carbon objectives. To address this issue, the problem is characterized in this study as an event-driven, dynamic spatiotemporal resource allocation and cost-coordinated optimization problem. A dynamic berth scheduling model is established with the objective of minimizing the total cost composed of waiting cost, delay cost, and carbon emission cost, and the problem is further formulated as a Markov decision process. Considering that conventional experience replay mechanisms are insufficient in exploiting critical samples and often suffer from limited training stability in complex dynamic scenarios, this study introduces an experience quality evaluation mechanism and a dual-replay-buffer collaborative training strategy on the basis of the DDQN algorithm, thereby proposing the QER-DDQN algorithm. The experimental results indicate that, under highly complex and highly congested test scenarios, QER-DDQN shows relatively better cost-control performance, with the average total cost reduced by approximately 8.08%, 10.77%, 7.78%, and 2.43% compared with FCFS, GA, PER-DDQN, and DDQN, respectively. The ablation study and scheduling scheme analysis further demonstrate that the experience quality evaluation mechanism and the dual-replay-buffer collaborative training strategy help improve the utilization efficiency of critical samples and, to a certain extent, enhance the local shoreline utilization structure, thereby alleviating the accumulation of waiting time and the propagation of delays caused by local resource conflicts. Further low-carbon scenario analysis shows that carbon tax level and shore power coverage rate both affect berth scheduling outcomes and carbon emission performance, and that appropriate low-carbon policy constraints and shore power infrastructure configuration are conducive to promoting the coordinated optimization of port scheduling efficiency and emission reduction objectives.