Extending Sim# for simulation-based optimisation of semi-automated machinery. (2017)

• Record Type: Journal Article
• Authors: Karder, Johannes; Scheibenpflug, Andreas; Beham, Andreas; Wagner, Stefan; Affenzeller, Michael
• Is Part Of: International journal of simulation and process modelling. Volume 12:Number 6(2017)
• Page Start: 485
• Page End: 497
• Publication Date: 2017
• Subjects: simulation-based optimisation -- genetic algorithms -- machinery -- Sim# -- HeuristicLab; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

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.