This project is a cooperation with the Ford Motor Company and the Max Planck Institut for Informatics.
In a car manufacturing process, the order of production is crucial for every production step, especially for the final assembly, where all pre-produced parts of the car are put together.
While more recent factories have a more modern structure, such that there is a generous number of options to optimize this production order, in the Ford factory in Saarlouis, the options to optimize the production order are limited by the line buffer, which can store and take out cars from one of 27 different lines. Currently, the optimization in Saarlouis is done manually by following some rules of thumbs.
In our project, we try to compensate for this structural disadvantage with intelligent control of the line buffer. We use deep reinforcement learning to train neural networks by self-play. These neural networks are supposed to take over control of the line buffer.
As deep reinforcement learning is known to be data consuming, it is no option to train the neural networks while using them in the factory. Instead, we use discrete event simulation to construct a digital twin ofi the line buffer and of the final assembly. Therefore, we can use this simulation to train the agents for optimizing car production in Saarlouis.
This work has been funded by the European Regional Development Fund (ERDF).