Abstract: In order to improve the scheduling efficiency of automatic stacking crane (ASC) in the synchronous mode of container storage and retrieval at automated container terminal (ACT), the scheduling of uncrossable twin ASC in the same block is investigated, with the objective of minimizing the maximum ASC completion time, a twin ASC scheduling mixed integer programming (MIP) model is established, which considers both the constraints of the capacity and location of the handshake area, and the mixed stacking of storage and retrieval tasks on the seaside and landside of the yard. The chaotic discrete particle swarm optimization (CDPSO) algorithm with permutation coding is designed with the sequence of operational tasks as the coding, and the convergence speed of the algorithm is improved by the designed sequence of chaotic factors. Three sets of experiments are designed by changing different handshake area location and capacity, and the experimental results show that the optimal handshake area location and capacity help to reduce the maximum completion time and conflict time of twin ASC, and meanwhile, the comparison experiments of different algorithms show that the designed algorithm can effectively solve the twin ASC scheduling MIP model, and the optimal solution obtained has better no-load time and maximum completion time of twin ASC compared with other algorithms.

Key words: automated container terminal (ACT), automated stacking crane (ASC), chaotic discrete particle swarm optimization (CDPSO), twin ASC scheduling

摘要： 为了提高自动堆垛起重机（ASC）在自动化集装箱码头（ACT）存取箱同步模式下的调度效率，对不可跨越式双ASC在同一箱区的调度进行了研究，考虑了堆场海侧和陆侧存箱、取箱任务的混合堆放，同时考虑握手区容量和位置约束，建立了以最小化最大ASC完工时间为目标的双ASC调度混合整数规划（MIP）模型。以作业任务顺序为编码设计了排列编码的混沌离散粒子群优化算法（CDPSO），通过设计的混沌因子序列提高了算法收敛速度。通过改变不同的握手区位置和容量设计了三组实验，实验结果表明，最优的握手区位置和容量有助于降低双ASC的最大完工时间、冲突时间，同时，不同的算法对比实验表明所设计的算法可以有效求解双ASC调度混合整数规划模型，相较于其他算法，得到的最优解具有更优的双ASC的空载时间和最大完工时间。

关键词: 自动化集装箱码头（ACT）, 自动堆垛起重机（ASC）, 混沌离散粒子群优化算法（CDPSO）, 双ASC调度