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Peer-reviewedCC BYScientific Reports

Digital twin for scenario-based design evaluation of manufacturing robotic fleets and factory layouts

  • Sepideh Valiollahi1
  • Ignacio Rodriguez2
  • Stefan Nordborg Eriksen1
  • Weifan Zhang1
  • Sebastian Damsgaard1
  • Preben E. Mogensen1
  1. 1Wireless Communication Networks Section, Department of Electronic Systems, Aalborg University
  2. 2Area of Signal Theory and Communications, Department of Electrical Engineering, University of Oviedo
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Abstract

Digital twin (DT) technology is increasingly used in industrial systems to support data-driven decision making, reduce human intervention, and improve coordination in automated environments. This study investigates how a DT-based simulation framework can support scenario-based design evaluation of production throughput by jointly modeling production processes, factory layouts, and heterogeneous robotic fleets with specialized roles such as transportation and manipulation. The proposed framework provides a high-fidelity representation of robotic operations, workflow dynamics, and spatial constraints across both brownfield and greenfield manufacturing environments. In brownfield settings, constrained by legacy infrastructure, the framework reveals scalability limits of robotic fleets and diminishing returns due to congestion. In greenfield environments, the framework enables systematic evaluation of layout configurations, fleet sizes, and functional role balance. The results show that bottlenecks may arise not only from spatial constraints but also from imbalances within the robotic fleet. In a representative case study, increasing fleet size in brownfield layouts yields limited improvements, whereas balanced configurations in greenfield layouts achieve up to 3.5 × throughput gains without reducing productivity per robot.

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Cite

BibTeXRIS
BibTeX
@article{valiollahi2026digital,
  title = {{Digital twin for scenario-based design evaluation of manufacturing robotic fleets and factory layouts}},
  author = {Sepideh Valiollahi and Ignacio Rodriguez and Stefan Nordborg Eriksen and Weifan Zhang and Sebastian Damsgaard and Preben E. Mogensen},
  journal = {Scientific Reports},
  year = {2026},
  month = jul,
  doi = {10.1038/s41598-026-57316-5},
  url = {https://doi.org/10.1038/s41598-026-57316-5},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Digital twin for scenario-based design evaluation of manufacturing robotic fleets and factory layouts
AU  - Sepideh Valiollahi
AU  - Ignacio Rodriguez
AU  - Stefan Nordborg Eriksen
AU  - Weifan Zhang
AU  - Sebastian Damsgaard
AU  - Preben E. Mogensen
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-57316-5
UR  - https://doi.org/10.1038/s41598-026-57316-5
AB  - Digital twin (DT) technology is increasingly used in industrial systems to support data-driven decision making, reduce human intervention, and improve coordination in automated environments. This study investigates how a DT-based simulation framework can support scenario-based design evaluation of production throughput by jointly modeling production processes, factory layouts, and heterogeneous robotic fleets with specialized roles such as transportation and manipulation. The proposed framework provides a high-fidelity representation of robotic operations, workflow dynamics, and spatial constraints across both brownfield and greenfield manufacturing environments. In brownfield settings, constrained by legacy infrastructure, the framework reveals scalability limits of robotic fleets and diminishing returns due to congestion. In greenfield environments, the framework enables systematic evaluation of layout configurations, fleet sizes, and functional role balance. The results show that bottlenecks may arise not only from spatial constraints but also from imbalances within the robotic fleet. In a representative case study, increasing fleet size in brownfield layouts yields limited improvements, whereas balanced configurations in greenfield layouts achieve up to 3.5 × throughput gains without reducing productivity per robot.
SN  - 2045-2322
ER  -