A knowledge-based approach to automated simulation model adaptation

Simulation has become a widely accepted technology for analyzing or planning systems in various domains. In production logistics, for instance, many companies use simulation to evaluate scenarios before actually the construction or modifications of the production hall or processes are performed in order to get insights about the performance of planned configurations. In this paper, we propose an approach to knowledge-based adaptation of simulation models. The vision of this work is to go one step beyond parameter optimization, namely to provide means for automated structural changes in simulation models, and thus for the generation of simulation model variants. For a first evaluation of our approach, we introduce a system consisting of a simulation control as well as a model adaptation module with a set of adaptation operations. Our implementation is coupled to the simulation system Plant Simulation in order to perform simulation runs. For illustration we apply our system to a test scenario and present first results.