平面移动式立体车库进行批量出入库作业时，其中系统指令的顺序不同以及有轨制导车（rail guided vehicle, RGV）的滞留位置会改变载车电梯与RGV的交互时间以及RGV的利用率高低，从而影响整个系统的出入库作业时间。为减少立体车库作业时间，针对平面移动式立体车库的作业流程，通过建立实时库位信息模型，载车电梯与RGV协同作业模型，并在动态时间阈值下对RGV出库指令中库位坐标进行调整。然后以作业时间为目标函数，提出一种改进灰狼算法进行求解。仿真结果表明：与原始灰狼算法与遗传算法相比，改进灰狼算法在寻优与鲁棒性方面具有更好的优异性能，并能有效缩短平面移动式立体车库的作业时间，提高了出入库效率。

When conducting batch inbound and outbound operations in a planar mobile three-dimensional garage, the different order of system commands and the detention position of rail guided vehicles (RGVs) can change the interaction time between the elevator and RGVs, as well as the utilization rate of RGVs, which will affect the overall inbound and outbound operation time of the system. To reduce the operation time of the three-dimensional garage, a real-time warehouse location information model was established for the operation process of the planar mobile three-dimensional garage. A collaborative operation model between the car elevator and the RGV was established, and the warehouse location coordinates in the RGV outbound command were adjusted under dynamic time thresholds. Then, taking the homework time as the objective function, an improved grey wolf algorithm was proposed for solving. The simulation results show that compared with the original grey wolf algorithm and genetic algorithm, the improved grey wolf algorithm has better performance in optimization and robustness, and can effectively shorten the operation time of the planar mobile three-dimensional garage, improving the efficiency of inbound and outbound operations.