LineFlow: A Framework to Learn Active Control of Production Lines

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.

Modern production lines often rely on complex control strategies—like adaptive routing,rescheduling, or reallocating workers—to stay efficient. Designing these control systems isdifficult, especially as production lines grow more automated and interconnected. Reinforcementlearning (RL) has shown promise in learning such control policies, but researchers lack astandardized, flexible framework to test and develop solutions. To address this, we developed LineFlow, an open-source Python toolkit that simulates realistic production lines and allows RL agents to interact with them. LineFlow supports highly customizablesetups and we study several core subproblems designed to be mathematically tractable. For each, weprovide optimal solutions so RL agents can be benchmarked reliably. We show that current RL methods perform well in controlled scenarios but struggle in more realistic, industrial-scale settings. This gap reveals important open challenges in applying RL to real-worldmanufacturing. Our work offers the community a testbed to explore these challenges and build more capable RLsystems for industrial control—bringing us one step closer to smarter, self-optimizing factories.