Abstract: To improve the handling efficiency of the railway container terminal and to reduce the long-distance movement of gantry cranes,a cooperative handling scheme named “gantry cranes and trucks” was proposed.Considering the three factors of container position,full-load and empty-load of gantry cranes,a bi-layer optimization model was developed for gantry crane dynamic allocation and integrated scheduling with trucks.The maximum balance rate was taken as the upper objective to assign the tasks of gantry cranes dynamically.The minimum completion time was considered as the lower objective to coordinate schedule the gantry cranes and trucks.According to the characteristics of the problem,combined with the overall evolution of the genetic algorithm and the population collaboration of the co-evolutionary algorithm,a hybrid co-evolutionary genetic algorithm was designed to solve model,and the validity was verified by numerical experiments.Calculation results showed that the proposed algorithm had a better search ability,and dynamic gantry crane allocation and integrated scheduling with trucks could effectively improve the operation balance rate and operation efficiency of the railway container terminal.

Key words: railway container terminal, integrated scheduling, dynamic allocation, crane area, gantry crane, genetic algorithm, co-evolutionary algorithm

摘要： 为提高铁路场站集装箱装卸效率和减少轨道吊长距离移动,提出“轨道吊—集卡”协同装卸方案,综合考虑集装箱位置、轨道吊重载与空载操作3个因素,构建轨道吊动态配置及其与集卡协同调度的双层优化模型。上层目标为作业均衡率最大化,解决轨道吊任务动态分配问题;下层目标为最小化作业完工时间,协同调度轨道吊与集卡。针对问题自身特点,结合遗传算法的整体进化和协同进化算法的种群协同,设计了混合协同进化遗传算法求解模型,并通过算例验证了模型与算法的有效性。结果表明,该算法具有更好的寻优能力;轨道吊动态配置及其与集卡的协同调度可有效提高铁路场站的作业均衡率和作业效率。

关键词: 铁路场站, 协同调度, 动态配置, 作业区域, 轨道吊, 遗传算法, 协同进化算法