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A study on multi-ASC scheduling method of automated container terminals based on graph theory

机译:基于图论的集装箱自动码头多ASC调度方法研究

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摘要

Compared with traditional terminals, two automated stacking cranes (ASCs) are configured for each container block of automated container terminal (ACT), which interact with automated guided vehicles (AGVs) and container trucks at the two ends of a container block individually. To increase the capacity, container yards with multiple rows of blocks perpendicular to the terminal's shoreline are considered. To utilize the yard spaces, twin ASCs are set to share transport tracks installed at the two sides of a block, while interferences between the ASCs causes the routing and sequencing operations. In order to control the scheduling of twin ASCs, the interference model is established by analyzing the time overlap between tasks. Considering the influence of AGV transportation time, the model are then established to sequence the container handling tasks under the minimization of waiting time and makespan. A particle swarm optimization algorithm (PSO) based on graph theory model is design to solve the problem. Numerical experiments show that the algorithm is more competitive than traditional algorithm. Based on the model and experimental result, the practical significance of applying the algorithm to the actual situation is discussed.
机译:与传统码头相比,自动化集装箱码头(ACT)的每个集装箱区都配置了两个自动堆垛起重机(ASC),它们分别与自动化引导车(AGV)和位于集装箱区两端的集装箱卡车相互作用。为了增加容量,考虑了具有多排垂直于码头海岸线的街区的集装箱堆场。为了利用堆场空间,设置了两个ASC来共享安装在块两侧的运输轨道,而ASC之间的干扰会导致路由和排序操作。为了控制双ASC的调度,通过分析任务之间的时间重叠来建立干扰模型。考虑到AGV运输时间的影响,然后建立模型以在最小化等待时间和有效期的情况下对集装箱搬运任务进行排序。针对该问题,设计了一种基于图论模型的粒子群算法。数值实验表明,该算法比传统算法更具竞争力。基于模型和实验结果,讨论了将该算法应用于实际情况的实际意义。

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