LineFlow: A Framework to Learn Active Control of Production Lines

TL;DR

LineFlow is an open-source framework that simulates complex production lines and trains reinforcement learning agents for active control, demonstrating near-optimal performance in simple cases but highlighting challenges in industrial-scale applications.

Contribution

The paper introduces LineFlow, a versatile framework for simulating production lines and benchmarking RL control algorithms, with theoretical analysis and optimal solutions for core subproblems.

Findings

RL policies approach optimal in simple scenarios

Challenges remain for complex, industrial-scale lines

Highlights need for advanced RL techniques like hierarchical control

Abstract

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.