Sequential Monte Carlo-based fidelity selection in dynamic-data-driven adaptive multi-scale simulations

Nurcin Celik, Young Jun Son

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement, a dynamic-data-driven adaptive multi-scale simulation (DDDAMS) paradigm was proposed earlier, where the fidelity of a complex simulation model adapts to available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. In this work, a sequential Monte Carlo method (sequential Bayesian inference technique) is proposed and embedded into the simulation to enable its ideal fidelity selection given massive datasets under the DDDAMS framework. As dynamic information becomes available, the proposed method makes efficient inferences to determine the sources of abnormality in the system (a shop floor in this paper). A parallelisation framework is also discussed to further reduce the number of data accesses while maintaining the accuracy of parameter estimates. A prototype DDDAMS involving the proposed algorithm has been implemented successfully for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, reducing the average waiting time of batches and increasing the machine utilisation significantly.

Original languageEnglish (US)
Pages (from-to)843-865
Number of pages23
JournalInternational Journal of Production Research
Issue number3
StatePublished - Feb 1 2012


  • Bayesian inference
  • dynamic-data-driven simulations
  • fidelity selection
  • multi-scale simulations
  • particle filter

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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