An ordinal transformation framework for multi-fidelity simulation optimization

Jie Xu, Si Zhang, Edward Huang, Chun Hung Chen, Loo Hay Lee, Nurcin Celik

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

10 Scopus citations

Abstract

Simulation models of different levels of fidelity are often available for evaluating alternative solutions of a complex system. High-fidelity simulations generate accurate predictions but can be very time-consuming to run. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are much faster and can evaluate a large number of solutions. But simulation results may contain significant bias and variability. We propose a novel ordinal transformation framework to exploit the benefits of both high- and low-fidelity simulation models to efficiently identify a (near) optimal solution. A two-stage simulation optimization method under the ordinal transformation framework is described. Through preliminary theoretical analysis and numerical experiments, we demonstrate the promising performance of ordinal transformation, which opens up a new and potentially fruitful research avenue.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Automation Science and Engineering
PublisherIEEE Computer Society
Pages385-390
Number of pages6
Volume2014-January
DOIs
StatePublished - 2014
Event2014 IEEE International Conference on Automation Science and Engineering, CASE 2014 - Taipei, Taiwan, Province of China
Duration: Aug 18 2014Aug 22 2014

Other

Other2014 IEEE International Conference on Automation Science and Engineering, CASE 2014
CountryTaiwan, Province of China
CityTaipei
Period8/18/148/22/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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