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LineFlow: A Framework to Learn Active Control of Production Lines

Kai Müller, Martin Wenzel, Tobias Windisch

TL;DR

LineFlow addresses the challenge of learning active control for production lines by providing an extensible discrete-event simulation framework that trains RL agents to optimize line performance. It formalizes subproblems WT, WTJ, PD_k, WA_{k,N}, and CL, and supplies optimal references for comparison, enabling rigorous benchmarking; RL policies are evaluated by maximizing $C_\pi(T_{sim})$ or equivalently by minimizing time to an objective. The experiments show RL can reach optimal performance in simple cases but require curriculum learning and memory-based strategies for complex lines, with sim-to-real validation on Bosch data supporting realism. Overall, LineFlow offers a standardized platform to accelerate RL-based active line control research and guide practical deployment.

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.

LineFlow: A Framework to Learn Active Control of Production Lines

TL;DR

LineFlow addresses the challenge of learning active control for production lines by providing an extensible discrete-event simulation framework that trains RL agents to optimize line performance. It formalizes subproblems WT, WTJ, PD_k, WA_{k,N}, and CL, and supplies optimal references for comparison, enabling rigorous benchmarking; RL policies are evaluated by maximizing or equivalently by minimizing time to an objective. The experiments show RL can reach optimal performance in simple cases but require curriculum learning and memory-based strategies for complex lines, with sim-to-real validation on Bosch data supporting realism. Overall, LineFlow offers a standardized platform to accelerate RL-based active line control research and guide practical deployment.

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.
Paper Structure (39 sections, 16 equations, 23 figures, 3 tables)

This paper contains 39 sections, 16 equations, 23 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Simulating Production Lines
  4. Objects of Production Lines
  5. Active Line Control
  6. Performance Measurements
  7. RL for Active Line Control
  8. Episodes and Rewards
  9. States, Observations, and Actions
  10. Implementation Details
  11. Case Studies
  12. Optimal Waiting Time $\mathrm{WT}$ and $\mathrm{WTJ}$
  13. Optimal Part Distributions $\mathrm{PD}_k$
  14. Optimal Worker Assignments $\mathrm{WA}_{k,N}$
  15. Complex Line $\mathrm{CL}$
  16. ...and 24 more sections

Figures (23)

  • Figure 1: Active line control based on real-time data.
  • Figure 2: A production line visualized with LineFlow.
  • Figure 3: Representatives of the three atomic production line challenges analyzed in this case study.
  • Figure 4: The jumps in the processing time of the assembly $A$ in $\mathrm{WTJ}$ for different simulations of length $4000$.
  • Figure 5: Scenario $\mathrm{CL}$ with $8$ assemblies.
  • ...and 18 more figures