lineflow-rl 0.1.0

Python tool to simulate assembly lines.

LineFlow

LineFLow is a python framework to simulate assembly lines. It allows to model arbitrary discrete part assembly lines and provides an gymnasium environment to optimize them with reinforcement learning. The documentation can be found here.

Install

Install with

pip install lineflow-rl

Examples

Visualization

This is how an assembly line can be implemented and visualized:

from lineflow.simulation import Line, Source, Sink, Process

class SimpleLine(Line):

    def build(self):

        # Set up stationary objects
        source = Source(
            name='Source',
            processing_time=5,
            position=(100, 500),
            unlimited_carriers=True,
        )

        process = Process('Process', processing_time=6, position=(350, 500))
        sink = Sink('Sink', processing_time=3, position=(600, 500))
        
        # Wire them with buffers
        source.connect_to_output(station=process, capacity=3)
        process.connect_to_output(station=sink, capacity=2)


line = SimpleLine()
line.run(simulation_end=500, visualize=True)

df = line.get_observations()

Training RL agents

This is how an RL agent can be trained using LineFlow:

from stable_baselines3 import PPO
from lineflow.simulation import LineSimulation

line = SimpleLine()
env = LineSimulation(line, simulation_end=100)
model = PPO("MlpPolicy", env)
model.learn(total_timesteps=100)

Docs

Serve the docs with

mkdocs serve

Paper

If you use our work in your research, please consider citing us with

@misc{LineFlow,
      title={LineFlow: A Framework to Learn Active Control of Production Lines}, 
      author={Kai Müller and Martin Wenzel and Tobias Windisch},
      year={2025},
      eprint={2505.06744},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.06744}, 
}

See this README for more details how to run the benchmarks.

Funding

The research behind LineFlow is funded by the Bavarian state ministry of research. Learn more here.