Calibrating simulation models using the knowledge gradient with continuous parameters

Warren R. Scott, Warren Buckler Powell, Hugo P. Simão

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

9 Scopus citations

Abstract

We describe an adaptation of the knowledge gradient, originally developed for discrete ranking and selection problems, to the problem of calibrating continuous parameters for the purpose of tuning a simulator. The knowledge gradient for continuous parameters uses a continuous approximation of the expected value of a single measurement to guide the choice of where to collect information next. We show how to find the parameter setting that maximizes the expected value of a measurement by optimizing a continuous but nonconcave surface. We compare the method to sequential kriging for a series of test surfaces, and then demonstrate its performance in the calibration of an expensive industrial simulator.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 Winter Simulation Conference, WSC'10
Pages1099-1109
Number of pages11
DOIs
StatePublished - Dec 1 2010
Event2010 43rd Winter Simulation Conference, WSC'10 - Baltimore, MD, United States
Duration: Dec 5 2010Dec 8 2010

Publication series

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

Other

Other2010 43rd Winter Simulation Conference, WSC'10
CountryUnited States
CityBaltimore, MD
Period12/5/1012/8/10

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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    Scott, W. R., Powell, W. B., & Simão, H. P. (2010). Calibrating simulation models using the knowledge gradient with continuous parameters. In Proceedings of the 2010 Winter Simulation Conference, WSC'10 (pp. 1099-1109). [5679082] (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2010.5679082