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A GENETIC ALGORITHM FOR THE STEEL CONTINUOUS CASTING WITH INTER-SEQUENCE DEPENDENT SETUPS AND DEDICATED MACHINES

机译:具有序列间相关设置和专用机器的钢连续铸造的遗传算法

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摘要

The steel continuous casting planning and scheduling problem namely SCC is a particular hybrid (flexible) flowshop that includes stages: (i) the converters (CV), (ii) the refining stands (RS) and (iii) the continuous casting (CC) stages. In this paper we study the SCC with inter-sequence dependent setups and dedicated machines at the last stage. The batch sequences are assumed to be pre-determined for one of the CC devices with a non preemptive scheduling process. The aim is to schedule the batches for each CC machine including the times setup between two successive sequences. We model the problem as a MILP where the objective is to minimize the makespan C _(max)that we formulate as the largest completion time taking account of the setup times for each CC. Then, we propose an adapted genetic algorithm that we call Regeneration GA (RGA) to solve the problem. We use a randomly generated instances of several sizes to test the model and for which we do not know an optimal solution. The method is able to solve the problems in an acceptable time for medium and large instances while a commercial solver was able to solve only small size instances.
机译:钢铁连铸计划和调度问题(即SCC)是一种特殊的混合(柔性)流程车间,包括以下阶段:(i)转炉(CV),(ii)精炼机架(RS)和(iii)连铸(CC)阶段。在本文中,我们将在最后阶段使用序列间相关的设置和专用机器来研究SCC。假定批处理序列是使用非抢先式调度过程为CC设备之一预先确定的。目的是为每台CC机器安排批次,包括两个连续序列之间的设置时间。我们将问题建模为MILP,其目的是最小化我们将公式化为最大完成时间的makepan C _(max),其中考虑到每个CC的建立时间。然后,我们提出了一种适应性遗传算法,称为再生GA(RGA)以解决该问题。我们使用随机生成的几种大小的实例来测试模型,而我们不知道该最优解决方案。该方法能够在可接受的时间内解决大中型实例的问题,而商用求解器只能解决小型实例。

著录项

  • 来源
    《RAIRO Operation Research》 |2018年第5期|1351-1376|共26页
  • 作者单位

    Université Le Havre Normandie, LMAH;

    Audencia Business School, 8 route de la Jonelière;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 04:09:42

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