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@PHDTHESIS{Delbrgger:862994,
      author       = {Delbrügger, Tim},
      othercontributors = {Roßmann, Heinz-Jürgen and Rehof, Jakob},
      title        = {{D}igital twin based decision support for factory
                      adaptation planning},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2023-00656},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2022},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2023; Dissertation, Rheinisch-Westfälische
                      Technische Hochschule Aachen, 2022},
      abstract     = {Factory adaptation planning is a highly complex,
                      interdisciplinary process which must be managed successfully
                      under high time pressure and with competing goals. Decision
                      support systems are one approach in making this process
                      sustainable and successful. For decision support, the
                      different disciplines often use specialized simulations to
                      compare planning alternatives. In general, planning
                      alternatives can differ by means of parametric variability
                      or structural variability. This thesis identifies three gaps
                      in the current state of research on 3D simulation-based
                      decision support systems that result in optimization over
                      structural variants being a largely manual process to date.
                      This research fills the gaps by using Digital Twins in order
                      to lay a foundation for novel 3D simulation-based decision
                      support systems that are also suitable for complex
                      structural factory adaptations. The new type of decision
                      support is characterized by the fact that Digital Twins are
                      implemented for relevant factory objects on different
                      hierarchical levels of the factory, each of which also
                      includes its own variability model. For example, a robot can
                      have different collision avoidance systems to choose from,
                      the robot workcell can have different types of robots, and
                      the factory floor can have diverse layout and material
                      transport variants. The combinations of these variants
                      result in a large number of possible configurations of the
                      factory, which are automatically searched by the decision
                      support system for candidates with desired metrics. As a
                      result, factory planners can specify desired metrics in a
                      Virtual Testbed and receive suggestions for suitable
                      configurations. The suggested configurations can be analyzed
                      directly in a 3D simulation to support interdisciplinary
                      communication effectively and efficiently. The distinctive
                      feature of this approach is that it reflects the actual need
                      of the factory planning team, which is to find Pareto
                      optimal candidates for the desired factory system among all
                      possible configurations, based on case-specific target
                      metrics. The suitability of the developed system
                      architecture and the underlying concepts is demonstrated by
                      prototypical application to three examples.},
      cin          = {615210},
      ddc          = {621.3},
      cid          = {$I:(DE-82)615210_20140620$},
      pnm          = {GRK 2193: Anpassungsintelligenz von Fabriken im dynamischen
                      und komplexen Umfeld (276879186)},
      pid          = {G:(GEPRIS)276879186},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2023-00656},
      url          = {https://publications.rwth-aachen.de/record/862994},
}