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Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm

机译:遗传算法在重构制造系统虚拟单元中的生产计划

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This paper focuses on the scheduling problem of the reconfiguration manufacturing system (RMS) for execution level, where the final objective is to output a production plan. The practical situation in Chinese factory is analyzed, and the characteristics are summarized into the contradiction between flow and job shop production. In order to handle this problem, a new production planning algorithm in virtual cells is proposed for RMS using an improved genetic algorithm. The advantages of this algorithm have three parts: (1) the virtual cell reconfiguration is formed to assist making production plans through providing relationship among task families and machines from cell formation; (2) The operation buffer algorithm is developed for flow style production in cells, which can realize the nonstop processing for flow style jobs; and (3) The multicell sharing method is proposed to schedule job shop jobs in order to fully utilize manufacturing capability among machines in multicells. Based on the above advantages, an improved genetic algorithm is developed to output scheduling plan. At last, the algorithm is tested in different instances with LINGO and the other genetic algorithm, and then the scheduling solution comparison shows the proposed algorithm can get a better optimum result with the same time using the comparison algorithm.
机译:本文关注于执行级别的重新配置制造系统(RMS)的调度问题,其中最终目标是输出生产计划。分析了中国工厂的实际情况,并将其特征概括为流程与车间生产之间的矛盾。为了解决这个问题,提出了一种新的虚拟单元生产计划算法,该算法采用改进的遗传算法进行RMS测量。该算法的优点包括三个部分:(1)通过提供任务族和来自单元形成的机器之间的关系,形成虚拟单元重新配置以协助制定生产计划; (2)开发了用于单元格中流式生成的操作缓冲算法,可以实现流式作业的不间断处理; (3)提出了一种多单元共享方法来调度车间作业,以充分利用多单元机器之间的制造能力。基于以上优点,开发了一种改进的遗传算法来输出调度计划。最后,将该算法与LINGO算法和另一种遗传算法在不同情况下进行了测试,然后通过调度方案的比较,表明该算法可以同时获得较好的最优结果。

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