The emergence of COVID-19 caused the stagnation of production activities and promoted the changing market demand. These uncertainties not only brought great challenges to the manufacturing approaches led by a single enterprise, but also threatened the stability of inherent supply chain. To maintain market competitiveness, an efficient distributed manufacturing resource allocation method is urgently needed by manufacturers. Cloud manufacturing (CMfg) is an advanced service-oriented manufacturing paradigm that breaks physical space constraints to integrate distributed resources across enterprises, and provides on-demand configuration of manufacturing services for personalized consumer needs in real-time. The focus of this paper is to achieve dynamic configuration of distributed resources in CMfg considering stochastic order arrival, while reducing overall completion time and improving resource utilization. First, a dynamic knowledge graph for distributed resources is constructed, and its definition and construction methods are introduced. Secondly, semantic matching between massive optional manufacturing resources and multiple types of subtasks is achieved through knowledge extraction, thereby obtaining a candidate set of manufacturing resources that meet basic requirements for each subtask. An artificial intelligence (AI) scheduler based on deep reinforcement learning is developed, and order urgency is incorporated into the design of state observation vectors. AI scheduler can generate optimal decision results based on environmental observations, select high-quality manufacturing service compositions over candidate sets, and ultimately achieve efficient distributed resources configuration. Finally, Dueling DQN-based training method is put forward to optimize AI scheduler, enabling adaptable decision-making performance in dynamic environment. In the experiment, a simulation environment with 18 different settings is designed that considers stochastic processing time, random order compositions and various order arrival patterns. The proposed graph-based matching method, scheduling policy learning method and dynamic decision-making method are tested in the simulation environment. The experiment results demonstrate that the cognitive and AI joint driven distributed manufacturing resource configuration method is superior to traditional methods in terms of policy learning speed and scheduling solution quality.

COVID-19的出现造成了生产活动的停滞，并推动了市场需求的变化。这些不确定性不仅给单一企业主导的制造方式带来了巨大挑战，也威胁到了固有供应链的稳定性。为了保持市场竞争力，制造商迫切需要一种高效的分布式制造资源配置方法。云制造（CMfg）是一种先进的面向服务的制造范式，它打破物理空间限制，整合跨企业的分布式资源，实时为个性化消费者需求提供制造服务的按需配置。本文的重点是在考虑随机订单到达的情况下实现 CMfg 中分布式资源的动态配置，同时减少总体完成时间并提高资源利用率。首先构建分布式资源动态知识图谱，介绍其定义和构建方法。其次，通过知识提取实现海量可选制造资源与多种类型子任务之间的语义匹配，从而获得满足每个子任务基本要求的制造资源候选集。开发了基于深度强化学习的人工智能（AI）调度器，并将订单紧迫性纳入状态观察向量的设计。AI调度器可以根据环境观测生成最优决策结果，在候选集中选择高质量的制造服务组合，最终实现高效的分布式资源配置。最后，提出了基于Dueling DQN的训练方法来优化AI调度器，实现动态环境中的自适应决策性能。在实验中，设计了具有18种不同设置的模拟环境，考虑了随机处理时间、随机订单组成和各种订单到达模式。所提出的基于图的匹配方法、调度策略学习方法和动态决策方法在仿真环境中进行了测试。实验结果表明，认知和人工智能联合驱动的分布式制造资源配置方法在策略学习速度和调度方案质量方面均优于传统方法。