Produktionsplanung bei Variantenfließfertigung

Boysen, Nils

doi:10.1007/978-3-540-69995-8_2

Cited by 5 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modular production systems are an up-and-coming solution to this challenge. In contrast to classical production lines, such as mixed-model assembly lines (Boysen 2007), modular production drops the concept of sequential processing and allows for more flexibility of routing diversified products through workstations 1 . This flexibility is inherently characterized by the application of automated guided vehicles (AGV).…”

Section: Introduction To Modular Production Systemsmentioning

confidence: 99%

Modular production control using deep reinforcement learning: proximal policy optimization

2021

View full text Add to dashboard Cite

EU regulations on $$\textit{CO}_2$$ CO 2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.

show abstract

Section: Introduction To Modular Production Systemsmentioning

confidence: 99%