LineFlow: A Framework to Learn Active Control of Production Lines
This work addresses the problem of designing active control systems for production lines, offering a general framework for researchers and practitioners, though it is incremental as it builds on existing RL methods without major breakthroughs.
The authors tackled the lack of a standardized framework for using reinforcement learning to actively control production lines, introducing LineFlow, an open-source Python tool that simulates production lines and trains RL agents, with learned policies approaching optimal performance in simple scenarios but facing challenges in complex industrial settings.
Many production lines require active control mechanisms, such as adaptive routing, worker reallocation, and rescheduling, to maintain optimal performance. However, designing these control systems is challenging for various reasons, and while reinforcement learning (RL) has shown promise in addressing these challenges, a standardized and general framework is still lacking. In this work, we introduce LineFlow, an extensible, open-source Python framework for simulating production lines of arbitrary complexity and training RL agents to control them. To demonstrate the capabilities and to validate the underlying theoretical assumptions of LineFlow, we formulate core subproblems of active line control in ways that facilitate mathematical analysis. For each problem, we provide optimal solutions for comparison. We benchmark state-of-the-art RL algorithms and show that the learned policies approach optimal performance in well-understood scenarios. However, for more complex, industrial-scale production lines, RL still faces significant challenges, highlighting the need for further research in areas such as reward shaping, curriculum learning, and hierarchical control.