LineFlow: A Framework to Learn Active Control of Production Lines

2025-05-10

AI Summary

Industrial production lines lack scalable, standardized reinforcement learning (RL) frameworks for proactive control—e.g., adaptive routing, worker reallocation, and dynamic scheduling. Method: We propose LineFlow, the first open-source, scalable, and theoretically analyzable unified framework integrating discrete-event simulation and RL-based control. It supports arbitrary-complexity simulations, incorporates state-of-the-art algorithms (e.g., PPO, SAC), and introduces reward shaping, curriculum learning, and hierarchical control modeling. Crucially, it provides analytical optimal solutions for key subproblems as theoretical baselines. Contribution/Results: Experiments demonstrate that learned policies achieve near-optimal performance in standard scenarios, validating framework efficacy and reproducibility. Moreover, empirical analysis uncovers fundamental bottlenecks in generalization and stability of current industrial RL methods, thereby addressing a critical gap in standardized tooling for smart manufacturing.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Standardized framework lacking for RL-based production line control
• Optimal active control solutions needed for complex production lines
• RL challenges persist in industrial-scale reward shaping and learning

Innovation

• LineFlow: open-source Python framework for production lines
• Trains RL agents for adaptive routing and rescheduling
• Benchmarks RL algorithms against optimal solutions