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Dynamic scheduling of manufacturing job shops using genetic algorithms

机译：使用遗传算法动态调度制造车间

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Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date. A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
机译：文献中报告的大多数车间作业调度方法通常解决静态调度问题。这些方法没有考虑多个条件，也没有容纳替代资源来处理作业。本文提出了一种基于遗传算法的调度方法，解决了上述所有缺点。遗传算法方法是一种调度置换方法，可以对随机生成的调度的初始池进行系统置换，以返回迄今为止找到的最佳调度。设计了一个动态调度问题，以紧密反映实际的车间调度环境。两种绩效衡量标准，即平均工作延迟和平均工作成本，被用来证明多准则调度。为了跨越变化的车间环境，确定了三个因素，每个因素在两个级别之间有所不同。大量模拟研究的结果表明，与几种常见的调度规则相比，遗传算法的调度方法具有更好的调度性能。

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