Abstract
In order to overcome the myopia problem, routing strategies must be based on formal representations of flow that automatically account for modifications in the values of parameters of interest and in the model itself. This work addresses this problem and discusses how to automatically incorporate resources (e.g. workstations/transportation devices/storage) in a Petri-net-derived model of flow that is modifiable at runtime to reflect and influence the routing in a manufacturing line. The modelling approach takes into consideration scalability needs and was experimentally validated. The applicability of the models is shown for PN-based dynamic scheduling.
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