Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

Yard handling is an important part of port logistics and affects the overall efficiency of the container port. The yard crane scheduling is affected by various external factors. For example, the dynamic cut-off time makes the release time of yard cranes variable, and the yard crane task arrangement will change frequently, resulting in a lot of computational time. To increase the flexibility of container yard handling, a twin yard cranes scheduling model is established considering the no-crossing constraints and the dynamic cut-off time. A joint scheduling of PSO and local re-scheduling strategy (LRPSO) is put forward to deal with the problem faster and more effectively. Small-scale and large-scale experiments are simulated to verify the performance of the proposed method. Results show that the scheduling method is more efficient.