Coordinated scheduling optimization of yard cranes and internal trucks at container terminals based on the NoisyNet-A3C algorithm

Container terminals constitute a critical component of port logistics systems, and their operational efficiency directly affects cargo turnover and terminal resource utilization. With the continuous growth in container throughput and terminal operating scale, coordination among quay cranes, yard cranes, and internal trucks has become increasingly complex, while equipment waiting and prolonged operation times constrain terminal efficiency. Therefore, efficient coordinated scheduling of yard cranes and internal trucks under quay crane operation sequence constraints is essential for improving terminal operational efficiency and resource utilization. This paper investigates the coordinated scheduling of yard cranes and internal trucks driven by the operational sequence of quay cranes in container terminal handling systems. To address the multi-objective optimization of minimizing yard crane operation time and internal truck waiting time, a mixed-integer linear programming model is developed, considering quay crane operation sequences, yard crane movement paths, and internal truck waiting times. The NoisyNet-A3C algorithm is introduced by incorporating learnable randomness into the parameter space to enhance adaptive exploration and prevent premature policy convergence. The proposed model and algorithm are validated using simulation cases and 30 consecutive days of operational data from Haitian Terminal at Xiamen Port. Simulation results demonstrate that the proposed method outperforms other deep reinforcement learning algorithms. The case study further shows that, compared with the terminal’s existing scheduling scheme, the proposed method reduces yard crane operation time by an average of 23.9% and internal truck waiting time by 21.7%, thereby reducing resource consumption in terminal operations. The results demonstrate that data-driven intelligent coordinated scheduling can support the digital transformation and operational energy optimization of port operations by improving equipment utilization and reducing unnecessary operations and waiting, while providing a practical pathway toward decarbonization and sustainable development of container terminals and shipping.