Abstract In the last years, the increase of data availability together with enhanced computation capabilities empowered researchers to conceive production planning and control methods with real-time inputs. Literature is rich with techniques for using simulation to take production planning and control decisions online. However, it is generally impractical to test these approaches on real systems, and experiments on digital instances are limited because they do not capture the physical aspects. This work proposes to test Real-time Simulation approaches using lab-scale models of manufacturing systems and a software architecture aligned with industrial standards. Such models allow to reproduce material flows and the production control logic of real factory environments. By exploiting this setting to test new approaches and tools, it is possible to increase their own achievable Technology Readiness Level (TRL). The laboratory has been used to set a real-time rescheduling problem on a Flexible Manufacturing System (FMS) model. The test involves simulation models aligned with the current system state for the online identification and implementation of a production scheduling rule that decreases the expected makespan. The results testify that the proposed lab-scale models can be used successfully to test production planning and control approaches.

中文翻译：

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用于测试实时仿真和生产控制技术的制造系统实验室规模模型

摘要 近年来，数据可用性的增加以及计算能力的增强使研究人员能够构思具有实时输入的生产计划和控制方法。文献中有丰富的技术，可以使用仿真在线进行生产计划和控制决策。然而，在真实系统上测试这些方法通常是不切实际的，并且在数字实例上的实验是有限的，因为它们没有捕获物理方面。这项工作建议使用制造系统的实验室规模模型和符合工业标准的软件架构来测试实时仿真方法。此类模型允许再现真实工厂环境的物料流和生产控制逻辑。通过利用此设置来测试新方法和工具，可以提高他们自己可实现的技术准备水平 (TRL)。该实验室已用于在柔性制造系统 (FMS) 模型上设置实时重新调度问题。该测试涉及与当前系统状态一致的仿真模型，用于在线识别和实施生产调度规则，从而减少预期的完工时间。结果证明，所提出的实验室规模模型可以成功地用于测试生产计划和控制方法。该测试涉及与当前系统状态一致的仿真模型，用于在线识别和实施生产调度规则，从而减少预期的完工时间。结果证明，所提出的实验室规模模型可以成功地用于测试生产计划和控制方法。该测试涉及与当前系统状态一致的仿真模型，用于在线识别和实施生产调度规则，从而减少预期的完工时间。结果证明，所提出的实验室规模模型可以成功地用于测试生产计划和控制方法。