堆垛机式密集仓储系统复合作业三维空间路径优化

Three-dimensional spatial path optimization for compound operation of stacker dense storage system

为提高堆垛机式密集仓储系统的运作效率,提出混合蚁群算法,利用该算法有效地解决了复合作业三维空间路径规划问题。在堆垛机和穿梭车配置比为1∶2情况下,分析堆垛机和穿梭车在三维空间内的实际调度路径,并考虑其运动过程中的加速度建立数学模型。针对该系统复合作业的特点,设计了一种三维启发函数来改进蚁群转移概率,将遗传算法生成的初始解转变为蚁群算法的初始信息素,通过粒子群算法对蚁群算法参数进行优化,避免蚁群算法为寻求最优参数组合而进行大量盲目实验。实例分析表明,所提出的混合蚁群算法与遗传和蚁群算法相比,具有更好的全局性,能够有效地缩短密集仓储系统复合作业的时间、优化三维空间路径、提高进出库调度效率。

To improve the operation efficiency of stacker dense storage system,the hybrid ant colony algorithm was used to solve the problem of compound operation effectively in three-dimensional space path planning.In the case of stacker and Rail Guided Vehicle（RGV）configuration ratio of 1：2,the actual scheduling path of stacker and RGV in three-dimensional space was analyzed,and the mathematical model was established by considering the acceleration in the process of motion.Aiming at the characteristics of system’s compound operation,a three-dimensional heuristic function was designed to improve the probability of ant colony transition.The initial solution generated by genetic algorithm was transformed into the initial pheromone of ant colony algorithm.To avoid seeking the optimal parameter combination of ant colony algorithm for a large number of blind experiments,the parameters were optimized with particle swarm algorithm.The example showed that the proposed algorithm had better overall performance by comparing with the genetic and ant colony algorithm,and could effectively shorten the compound operation time of the stacker dense storage system and improve the efficiency of the loading/unloading.