Developing composed simulation and optimization models using actual supply-demand network datasets

Large, fine-grain data collected from an actual semiconductor supply-demand system can help automated generation of its integrated simulation and optimization models. We describe how instances of Parallel DEVS and Linear Programming (LP) models can be semi-automatically generated from industry-scale relational databases. Despite requiring the atomic simulation models and the objective functions/constraints in the LP model to be available, it is advantageous to generate system-wide supply-demand models from actual data. Since the network changes over time, it is important for the data contained in the LP model to be automatically updated at execution intervals. Furthermore, as changes occur in the models, the interactions in the Knowledge Interchange Broker (KIB) model, which composes simulation and optimization models, are adjusted at run-time.