The emergence of Industry 4.0 is making production systems more flexible and also more dynamic. In these settings, schedules often need to be adapted in real-time by dispatching rules. Although substantial progress was made until the '90s, the performance of these rules is still rather limited. The machine learning literature is developing a variety of methods to improve them, but the resulting rules are difficult to interpret and do not generalise well for a wide range of settings. This paper is the first major attempt at combining machine learning with domain problem reasoning for scheduling. The idea consists of using the insights obtained with the latter to guide the empirical search of the former. Our hypothesis is that this guided empirical learning process should result in dispatching rules that are effective and interpretable and which generalise well to different instance classes. We test our approach in the classical dynamic job shop scheduling problem minimising tardiness, which is one of the most well-studied scheduling problems. Nonetheless, results suggest that our approach was able to find new state-of-the-art rules, which significantly outperform the existing literature in the vast majority of settings, from loose to tight due dates and from low utilisation conditions to congested shops. Overall, the average improvement is 19%. Moreover, the rules are compact, interpretable, and generalise well to extreme, unseen scenarios.
翻译:工业4.0的出现使生产系统更加灵活,也更具活力。在这些环境中,时间表往往需要通过发送规则来实时调整。虽然在90年代之前已经取得了很大进展,但这些规则的绩效仍然相当有限。机器学习文献正在开发各种改进方法,但由此产生的规则很难解释,而且无法对广泛的各种环境进行概括化。本文是将机器学习与域问题推理结合起来的第一个重大尝试。想法是利用从后者获得的洞察力来指导前者的经验搜索。我们的假设是,这一指导性的经验学习过程应导致发布有效和可解释的规则,这些规则应广泛适用于不同的实例类别。我们在典型的动态商店时间安排问题上测试了我们的方法,以尽量减少延误,这是经过最深入研究的时间安排问题之一。然而,结果显示,我们的方法能够找到新的先进规则,这些规则大大超越了大多数环境中的现有文献,从较紧的到期日期到较紧的截止日期,以及从低的缩缩缩版规则到最接近的版本。 总体而言,改进了19 总体情况是 。