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Simulation of Innovative Production Environments
Besides the simulation of conventional production lines, acp-IT also offers model development for innovative production environments. The shop floor employment of automated guided vehicles (AGVs), equipped with robot arms and controlled by a manufacturing execution system (MES), was already successfully modeled and analyzed (see also pictures).
Simulation of an innovative production environment with extended AGVs
Companies from manufacturing industry not just have to deal with the classic target conflict between time, quality and costs they also have to consider higher customer requirements arising from shortened product life cycles and desired product individualizations. For this reason, the innovation pressure in the manufacturing domain is increased considerably. The well-know approaches to handle the classic target conflict – to maximize production automation, e.g. by conveyors or AGVs – prune flexibility and therefore don't take sufficiently frequent product changes and required product individualizations into account. The above mentioned combination of AGVs with robot arms and a MES, which was developed in the Amadeus research project, could be one solution. The subsequent picture shows the presentation of the Amadeus system at the Motek trade fair in Stuttgart.
Amadeus system, Motek trade fair, Stuttgart 2011
The proof of concept of an extended AGV with MES control was achieved with the demonstrator – the question regarding the benefit of the approach was still unanswered. It was decided to give the answer by conducting simulations, since the consideration of real-world scenarios with several Amadeus systems was not possible because of limited time and money. The simulation models for this purpose were developed by acp-IT with AnyLogic and its InFrame Synapse Simulation Suite. In doing so, agent-based models for three different forms of Amadeus systems and for the workers were implemented. The following figure depicts an extract of the statistics collected during model execution.
Statistics during model execution