Extending Sim# for simulation-based optimisation of Semi-Automated machinery

Johannes Karder, Andreas Scheibenpflug, Andreas Beham, Stefan Wagner, Michael Affenzeller

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Model building is a fundamental task in simulation-based optimisation. In this paper, we demonstrate the application of Sim# in combination with HeuristicLab to optimise semi-automated machinery. On top of Sim#, custom simulation extensions have been implemented and are used to create a simulation model of real world machinery. These extensions enable the design of simulation components that can be reused within different simulation models. This allows to easily create multiple model implementations that reflect different designs of a machine by using a combination of already existing and adapted components. The resulting model is used as an evaluation function for single-A nd multi-objective optimisation using HeuristicLab. Results for different optimisation targets, e.g., job order, and quality criteria such as setup time are compared.

Original languageEnglish
Pages (from-to)485-497
Number of pages13
JournalInternational Journal of Simulation and Process Modelling
Issue number6
Publication statusPublished - 2017


  • Genetic Algorithms
  • Heuristiclab
  • Machinery
  • Sim#
  • Simulation-Based Optimisation


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