Title: Extending Sim# for simulation-based optimisation of semi-automated machinery

Authors: Johannes Karder; Andreas Scheibenpflug; Andreas Beham; Stefan Wagner; Michael Affenzeller

Addresses: Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria; Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria; Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria; Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria

Abstract: 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- and multi-objective optimisation using HeuristicLab. Results for different optimisation targets, e.g., job order, and quality criteria such as setup time are compared.

Keywords: simulation-based optimisation; genetic algorithms; machinery; Sim#; HeuristicLab.

DOI: 10.1504/IJSPM.2017.089634

International Journal of Simulation and Process Modelling, 2017 Vol.12 No.6, pp.485 - 497

Received: 04 Apr 2016
Accepted: 30 Aug 2016

Published online: 23 Jan 2018 *

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