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An Adaptive Intelligent Method for Manufacturing Process Optimization in Steelworks

机译:一种自适应智能化方法,用于制造钢厂生产优化

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In this paper, the manufacturing process in iron and steel manufacturing systems is modeled as the m-machine no-wait flowshop scheduling problem with sequence-dependent setup times. To avoid extreme uneven utilization of machine capacity, makespan is considered as the minimization criterion. An adaptive intelligent method named GEIM is proposed, which is a new meta-heuristic on the basis of greedy randomized adaptive search procedure and evolutionary local search, for the considered problem. Path-relinking strategy is adopted to bridge the local optimum and elite solutions for exploring the solution spaces. Attributes of elite solutions are extracted to guide the local search process and an adaptive control mechanism is provided for the diversification of elite set at the end phase of the searching process. The proposed algorithm is compared with some existing approaches from the literature on classical benchmark instances. Computational results show that the proposed algorithm outperforms the existing ones and is suitable for optimizing the manufacturing process in steelworks.
机译:在本文中,钢铁制造系统中的制造过程被建模为具有序列依赖的设置时间的M机无等待流程调度问题。为避免极端不均匀利用机器容量,因此MakEspan被认为是最小化标准。提出了一种名为Geim的自适应智能方法,这是一个新的元启发式,基于贪婪随机的自适应搜索程序和进化本地搜索,用于考虑的问题。采用路径遥控策略来桥接局部最佳和精英解决方案来探索解决方案空间。提取精英解决方案的属性以指导本地搜索过程,并且为在搜索过程的结束阶段设置精英设置的自适应控制机制。将所提出的算法与来自古典基准实例的文献中的一些现有方法进行比较。计算结果表明,所提出的算法优于现有的算法,适用于优化钢厂中的制造过程。

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