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.Comment: Submitted and accepted version to the 5th International Data Science
Conference (iDSC), Krems, Austri