@inproceedings{UlmerBraunChengetal.2021,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Adapting Augmented Reality Systems to the users' needs using Gamification and error solving methods},
  series = {Procedia CIRP},
  volume    = {104},
  booktitle = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2021.11.024},
  pages     = {140 -- 145},
  year      = {2021},
  abstract  = {Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users' preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.},
  language  = {en}
}
@article{BraunChengDoweyetal.2021,
  author    = {Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Performance evaluation of skill-based order-assignment in production environments with multi-agent systems},
  series = {IEEE Journal of Emerging and Selected Topics in Industrial Electronics},
  journal   = {IEEE Journal of Emerging and Selected Topics in Industrial Electronics},
  number    = {Early Access},
  publisher = {IEEE},
  address   = {New York},
  issn      = {2687-9735},
  doi       = {10.1109/JESTIE.2021.3108524},
  year      = {2021},
  abstract  = {The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.},
  language  = {en}
}