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.
Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.
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