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首页> 外文期刊>Engineering Applications of Artificial Intelligence >A genetic algorithm approach for a single hoist scheduling problem with time windows constraints
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A genetic algorithm approach for a single hoist scheduling problem with time windows constraints

机译:具有时间窗约束的单机提升调度问题的遗传算法

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摘要

This study considers a cyclic scheduling of hoist movements in electroplating industry. Several jobs have to flow through a production line according to an ordered bath sequence. They firstly enter the line at a loading buffer. Then they will be soaked sequentially in a series of tanks containing specific chemical baths. Finally, they will leave the line at the unloading buffer. The processing time duration of each job in each tank is not constant but confined within a time window bounded by a minimum and a maximum duration. If a job spends less than the minimum duration or more than the maximum duration it is considered defective. Moreover, not only the job operations in the soaking tanks have to be scheduled, but also the transportation of the jobs between tanks has to be considered. The problem now is to find an optimum or near optimum feasible cyclic scheduling such that the hard resource and time-window constraints are respected and the cycle time duration is minimized. A mathematical formulation is proposed for the multi-jobs cyclic hoist scheduling problem with a single transportation resource, and a Genetic Algorithm (GA) approach is presented to solve it. The performance of the proposed algorithm is evaluated with the objective value obtained with a linear programming model, on several literature instances. Computational experiments show the good performance of our GA in terms of solution quality, convergence and computation time.
机译:本研究考虑了电镀行业中葫芦运动的周期性调度。根据订购的熔池顺序,一些作业必须流经生产线。他们首先在装载缓冲区进入生产线。然后将它们顺序浸泡在一系列装有特定化学浴的槽中。最后,他们将把行留在卸载缓冲区。每个槽中每个作业的处理持续时间不是恒定的,而是限制在以最小持续时间和最大持续时间为边界的时间窗口内。如果工作花费的时间少于最小持续时间或大于最大持续时间,则认为该工作有缺陷。此外,不仅必须安排浸泡槽中的作业操作,而且还必须考虑槽之间的作业运输。现在的问题是找到一个最佳的或接近最佳的可行循环调度,以便遵守硬资源和时间窗口约束,并使循环时间最小化。针对具有单一运输资源的多作业循环葫芦调度问题,提出了一种数学公式,并提出了一种遗传算法来解决。在一些文献实例中,使用线性规划模型获得的目标值对所提出算法的性能进行了评估。计算实验显示了我们的遗传算法在解决方案质量,收敛性和计算时间方面的良好性能。

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