Abstract: In the automated container terminal yard with a vertical layout, the collaborative scheduling and resource allocation of non-crossing Automated Stacking Cranes (ASC) are crucial for improving operation efficiency. The traditional fixed buffer block strategy is prone to causing path conflicts and resource competition bottlenecks due to the uneven distribution of tasks, resulting in limited operation efficiency. To address this issue, this paper proposes a joint optimization framework for the scheduling of two ASCs and the decision-making of flexible buffer blocks. The aim is to achieve efficient collaboration of yard resources through a dynamic task-block matching mechanism and a safety time interval constraint model. Firstly, a mixed-integer programming model is constructed. With the goal of minimizing the task completion time, it couples the path planning of ASCs, task sequences, and the dynamic allocation of buffer blocks. Secondly, a multi-chromosome encoding genetic algorithm is designed, and the sorting crossover and Gaussian mutation strategies are adopted to enhance the solution efficiency for large-scale instances. Numerical experiments show that compared with the single fixed buffer block strategy, the flexible buffer block mechanism can reduce the average task completion time by 13.9%, verifying the adaptability and accuracy of the dynamic allocation. This study provides theoretical support and decision-making basis for the resource allocation in automated terminal yards.