论文标题
学会在车间安排上概括派遣规则
Learning to generalize Dispatching rules on the Job Shop Scheduling
论文作者
论文摘要
本文介绍了一种增强学习方法，以更好地概括有关工作店调度问题（JSP）的启发式调度规则。 JSP上的当前模型并不关注概括，尽管正如我们在这项工作中所显示的那样，这是对问题进行更好的启发式方法的关键。提高概括的众所周知的技术是使用课程学习（CL）学习日益复杂的实例。但是，正如文献中许多作品所表明的那样，在不同问题大小之间传递学习技能时，这种技术可能会遭受灾难性的遗忘。为了解决这个问题，我们介绍了一种新颖的对抗性课程学习（ACL）策略，该策略在学习过程中动态调整了难度水平以重新审视表现最差的实例。这项工作还提出了一个深度学习模型来解决JSP，这是e var的W.R.T.作业定义和尺寸不合时宜。在Taillard's和Demirkol实例上进行了实验表明，提出的方法显着改善了JSP上当前的最新模型。它的平均最佳差距从Taillard实例的19.35 \％降低到10.46 \％，而Demirkol的实例中的平均最佳差距从38.43 \％降低到18.85％。我们的实施可在线提供。
This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, which dynamically adjusts the difficulty level during the learning process to revisit the worst-performing instances. This work also presents a deep learning model to solve the JSP, which is equivariant w.r.t. the job definition and size-agnostic. Conducted experiments on Taillard's and Demirkol's instances show that the presented approach significantly improves the current state-of-the-art models on the JSP. It reduces the average optimality gap from 19.35\% to 10.46\% on Taillard's instances and from 38.43\% to 18.85\% on Demirkol's instances. Our implementation is available online.
