Simulation model calibration with correlated knowledge-gradients

Peter Frazier, Warren B. Powell, Hugo P. Simão

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


We address the problem of calibrating an approximate dynamic programming model, where we need to find a vector of parameters to produce the best fit of the model against historical data. The problem requires adaptively choosing the sequence of parameter settings on which to run the model, where each run of the model requires approximately twelve hours of CPU time and produces noisy non-stationary output. We describe an application of the knowledge-gradient algorithm with correlated beliefs to this problem and show that this algorithm finds a good parameter vector out of a population of one thousand with only three runs of the model.

Original languageEnglish (US)
Title of host publicationProceedings of the 2009 Winter Simulation Conference, WSC 2009
Number of pages13
StatePublished - 2009
Event2009 Winter Simulation Conference, WSC 2009 - Austin, TX, United States
Duration: Dec 13 2009Dec 16 2009

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2009 Winter Simulation Conference, WSC 2009
Country/TerritoryUnited States
CityAustin, TX

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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