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Scheduling of lifting vehicles with time windows in an automated port container terminal

机译:在自动端口容器终端中将升降车辆的调度

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A port container terminal is generally exploited with three types of equipments: Quay Cranes (QCs), vehicles and Yard Cranes (YCs). In this paper, we present a particular type of vehicles which is used at terminal of Normandy in Le Havre port in France. In this terminal, containers are transported from the marshalling yard to ship by the lifting vehicles (LV), capable of raising and of depositing a single container at various heights. This study discusses how to schedule LVs in loading or unloading operation by using information about pickup and delivery locations. Our goal is to organize the transfer of containers in order to minimize the total travel time of all LV, respecting the time window of every LV for a given number of containers. In order to solve this problem we propose a new modeling and a genetic algorithm adapted to the studied problem called “Genetic Algorithm for Loading or Unloading Operation GALUO”. We applied special types of crossover and mutation inspired from various data of our problem. GALUO is compared with a simulated annealing algorithm (SA) inspired from the same problem. Computational results on real instances show the good quality of the solutions obtained by the proposed method.
机译:端口集装箱终端通常用三种类型的设备进行利用:码头起重机(QCS),车辆和院子里的起重机(YCS)。在本文中,我们提出了一种特定类型的车辆,该车辆在法国勒瓦尔港诺曼底的终端使用。在该终端中,容器从编组码运输到由提升车辆(LV)运输,能够在各种高度处升高和沉积单个容器。本研究讨论了如何通过使用有关拾取和交付位置的信息来加载或卸载操作中的LV。我们的目标是组织容器的转移,以最小化所有LV的总旅行时间,尊重给定数量的容器的每个LV的时间窗口。为了解决这个问题,我们提出了一种新的建模和一种遗传算法,适用于所谓的问题“遗传算法加载或卸载操作Galuo”我们应用了来自我们问题的各种数据的特殊类型的交叉和突变。将Galuo与来自同一问题的模拟退火算法(SA)进行比较。实际情况上的计算结果显示了所提出的方法获得的良好质量。

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