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A study on DAA-based crane scheduling models for steel plant

机译:钢铁厂基于DAA的起重机调度模型研究

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Crane scheduling tasks in steelworks are a matter of uncertainty scheduling with certain probability distribution pattern. To better schedule tasks with this feature, this paper proposes a Dynamic Area Allocation (DAA)-based crane scheduling model according to the following steps. First, Bayesian network, according to the time sequence of crane transportation tasks, is constructed. Then, conditional probability for each network node on the basis of actual crane operating data is calculated for getting the corresponding time-space probability distribution, and then obtaining the spatial distribution by superposing of all crane transportation tasks in the space domain at certain time. At last, tasks are assigned to cranes based on their spatial distribution and the equal probability partition. Simulation testing on the scheduling model is carried out using practical crane transportation tasks in steelworks. Results show that the model based on the dynamic area allocation, with its scheduling period of 15 min, can greatly shorten transportation time and reduce times of collision resulted from crane interference, after compared with the current widely used crane scheduling programme based on the fixed area allocation (FAA).
机译:钢结构中的起重机调度任务是具有某些概率分布模式的不确定性调度问题。要使用此功能更好地计划任务,本文根据以下步骤提出了基于动态区域分配(DAA)的起重机调度模型。首先,贝叶斯网络,根据起重机运输任务的时间顺序,是建造的。然后,计算基于实际起重机操作数据的每个网络节点的条件概率,以获得相应的时间空间概率分布,然后通过在特定时间叠加空间域中的所有起重机传送任务来获得空间分布。最后,基于它们的空间分布和相同的概率分区分配任务到起重机。调度模型上的仿真测试是在钢厂中使用实用的起重机运输任务进行的。结果表明,基于动态面积分配的模型,其调度期为15分钟,可以大大缩短运输时间,并在基于固定区域的电流广泛使用的起重机调度程序相比,缩短了起重机干扰导致的碰撞时间分配(FAA)。

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