Designing an adaptive and deep learning based control framework for modular production systems

Marcel Panzer () and Norbert Gronau
Additional contact information
Marcel Panzer: University of Potsdam
Norbert Gronau: University of Potsdam

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 26, 4113-4136

Abstract: Abstract In today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.

Keywords: Modular production; Production control; Deep learning; Reinforcement Learning; Simulation framework; Explainability (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02249-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02249-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02249-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().