This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and batch size one production. Specifically, we present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. Each simulation run involves placing a specific number of goods of random color at the entry point. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. Following the installation of rivets, blue products must be transported to the exit, while green products are to be transported to storage. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.

翻译：本应用论文探讨了利用强化学习（RL）解决工业4.0需求（包括缩短上市时间、大规模定制及批量为一的生产模式）的潜力。具体而言，我们提出一个应用案例：通过遵循预定义规则的模型工厂实现货物的运输与装配。每次仿真运行会在入口处放置特定数量的随机颜色货物。目标是将货物运送至装配站，在该站为每个产品安装两个铆钉以连接上下部件。铆钉安装完成后，蓝色产品需运往出口，绿色产品则运往仓库。本研究聚焦于应用强化学习技术解决该问题并提升生产流程效率。