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Learning dispatching rules using random forest in flexible job shop scheduling problems

机译:在灵活的作业商店调度问题中使用随机林学习调度规则

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In this paper, we address the flexible job-shop scheduling problem (FJSP) with release times for minimising the total weighted tardiness by learning dispatching rules from schedules. We propose a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) in order to extract dispatching rules from the best schedules. RANFORS consists of three phases: schedule generation, rule learning with data transformation, and rule improvement with discretisation. In the schedule generation phase, we present three solution approaches that are widely used to solve FJSPs. Based on the best schedules among them, the rule learning with data transformation phase converts them into training data with constructed attributes and generates a dispatching rule with inductive learning. Finally, the rule improvement with discretisation improves dispatching rules with a genetic algorithm by discretising continuous attributes and changing parameters for random forest with the aim of minimising the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach and the results showed that it outperforms the existing dispatching rules. Moreover, compared with the other decision-tree-based algorithms, the proposed algorithm is effective in terms of extracting scheduling insights from a set of rules.
机译:在本文中,我们通过释放时间来解决灵活的作业商店调度问题(FJSP),以便通过从计划中派遣规则来最小化总加权迟到。我们提出了一种被称为随机林的随机林的方法,用于获取调度(Ranfors)的规则,以便从最佳的时间表中提取调度规则。 Ranfors由三个阶段组成:安排生成,规则学习与数据转换,并通过自由定义来改进。在计划生成阶段,我们提出了三种解决方案方法,这些方法广泛用于解决FJSP。基于它们中的最佳时间表,具有数据转换阶段的规则学习将它们转换为具有构造属性的训练数据,并使用感应学习生成调度规则。最后,通过离散化的规则改进通过离散性的连续属性和随机森林的改变参数来提高与遗传算法的调度规则,目的是最小化平均总加权迟到。我们进行了实验以验证所提出的方法的表现,结果表明它表明它优于现有的调度规则。此外,与其他基于决策树的算法相比,所提出的算法在从一组规则中提取调度洞察方面是有效的。

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