Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

中文翻译：

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无序执行为动态增材制造调度启用深度强化学习


增材制造 (AM) 通过实现按需定制制造彻底改变了生产格局。然而，动态增材制造订单的高效管理给生产计划和调度带来了重大挑战。本文解决了考虑批处理、随机订单到达和机器资格约束的动态调度问题，旨在最大限度地减少并行非相同 AM 机器环境中的总延迟。为了解决这个问题，我们提出了乱序启用的决斗深度 Q 网络（O3-DDQN）方法。在所提出的方法中，问题被表述为马尔可夫决策过程（MDP）。使用“环视”方法提取三维特征，包括动态订单、增材制造机器和延迟，以表示重新调度点的生产状态。此外，还引入了五种基于无序原理的新型复合调度规则，供增材制造机器完成加工或新订​​单到达时选择。此外，我们设计了一个与评估智能体选择的行动的目标密切相关的奖励函数。实验结果证明了 O3-DDQN 方法相对于单一调度规则、随机选择规则和经典 DQN 方法的优越性。与复合调度规则和随机规则相比，性能平均提升率为13.09%。此外，O3-DDQN 的性能优于经典的 DQN 代理，改进率为 6.54%。 O3-DDQN 算法改进了动态 AM 环境中的调度，提高了生产率和准时交付。这项研究有助于推进增材制造生产，并提供有效资源分配的见解。