Dynamic-data-driven adaptive multi-scale simulation (DDDAMS) for planning and control of distributed manufacturing enterprises

Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective date update. Four algorithms are embedded into a real-time simulator for its DDDAMS capability, including data filtering algorithm, fidelity selection algorithm, fidelity assignment algorithm, and task generation algorithm. Grid computing and Web Services are used for computational resource management and inter-operable communications among distributed software components. The proposed DDDAMS is applied for operational scheduling and preventive maintenance scheduling in a semiconductor manufacturing supply chain.