DDDAS-based multi-fidelity simulation framework for supply chain systems

Abstract

Dynamic-Data-Driven Application Systems (DDDAS) is a new modeling and control paradigm which adaptively adjusts the fidelity of a simulation model. The fidelity of the simulation model is adjusted against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective date update. To this end, comprehensive system architecture and methodologies are first proposed, where the components include a real-time DDDAS simulation, grid modules, a web service communication server, databases, various sensors and a real system. Abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation are enabled through the embedded algorithms developed in this work. Grid computing is used for computational resources management and web services are used for inter-operable communications among distributed software components. The proposed DDDAS is demonstrated on an example of preventive maintenance scheduling in a semiconductor supply chain.

Keywords:

• Multi-fidelity simulation
• real-time simulation
• online maintenance scheduling
• semiconductor manufacturing
• distributed computing
• Bayesian inference
• simulation-based control

Acknowledgements

This work was supported by the National Science of Foundation under grant NSF 0540212.