Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.

有效控制材料的生产和释放到制造系统中可以降低在制品库存和周期时间，同时确保所需的吞吐量。通过从制造系统收集的大量数据，制定有效的实时控制策略有助于实现这一目标。在实施之前，可能无法使用真实的制造系统来验证新的控制方法。同样，使用仿真模型可能会导致忽略新控制方法性能的关键方面。为了克服这些缺点，使用给定制造系统的实验室规模物理模型可能是有益的。我们讨论了实验室规模物理模型的构建和使用，以研究数据驱动的生产控制策略在生产/库存系统中的实施。作为数据驱动的生产控制策略，使用标记相关阈值策略。该策略通过使用联合模拟和优化来利用从需求和生产过程中收集的部分信息来确定最佳阈值。我们通过使用 LEGO Technic 零件并使用从系统收集的数据使用标记相关策略控制模型来说明实验室规模模型的构建。通过直接从实验室规模的生产/库存系统收集数据，我们展示了系统的分析建模如何以及为什么在预测系统动态方面会出错，以及如何对其进行改进。这些错误会对使用这些模型的系统优化产生不利影响。相比下，本研究中提出的数据驱动方法受物理系统与其分析表示之间差异的影响要小得多。这些实验表明，使用实验室规模的制造系统环境对于在实施之前研究不同的数据驱动控制策略非常有用，并且标记相关阈值策略是优化制造系统中物料流的有效数据驱动策略。