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

Nurcin Koyuncu, Seungho Lee, Parag Sarfare, Young Jun Son

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Number of pages6
StatePublished - 2008
EventIIE Annual Conference and Expo 2008 - Vancouver, BC, Canada
Duration: May 17 2008May 21 2008


OtherIIE Annual Conference and Expo 2008
CityVancouver, BC


  • Dynamic information sharing
  • Grid computing
  • Multi-fidelity modeling
  • Simulation-based control

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Industrial and Manufacturing Engineering


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