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 Citations (Scopus)

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

Fingerprint

Large scale systems
Experiments

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Xu, J., Zhang, S., Huang, E., Chen, C. H., Lee, L. H., & Celik, N. (2014). An ordinal transformation framework for multi-fidelity simulation optimization. In IEEE International Conference on Automation Science and Engineering (Vol. 2014-January, pp. 385-390). [6899354] IEEE Computer Society. https://doi.org/10.1109/CoASE.2014.6899354

An ordinal transformation framework for multi-fidelity simulation optimization. / Xu, Jie; Zhang, Si; Huang, Edward; Chen, Chun Hung; Lee, Loo Hay; Celik, Nurcin.

IEEE International Conference on Automation Science and Engineering. Vol. 2014-January IEEE Computer Society, 2014. p. 385-390 6899354.

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

Xu, J, Zhang, S, Huang, E, Chen, CH, Lee, LH & Celik, N 2014, An ordinal transformation framework for multi-fidelity simulation optimization. in IEEE International Conference on Automation Science and Engineering. vol. 2014-January, 6899354, IEEE Computer Society, pp. 385-390, 2014 IEEE International Conference on Automation Science and Engineering, CASE 2014, Taipei, Taiwan, Province of China, 8/18/14. https://doi.org/10.1109/CoASE.2014.6899354
Xu J, Zhang S, Huang E, Chen CH, Lee LH, Celik N. An ordinal transformation framework for multi-fidelity simulation optimization. In IEEE International Conference on Automation Science and Engineering. Vol. 2014-January. IEEE Computer Society. 2014. p. 385-390. 6899354 https://doi.org/10.1109/CoASE.2014.6899354
Xu, Jie ; Zhang, Si ; Huang, Edward ; Chen, Chun Hung ; Lee, Loo Hay ; Celik, Nurcin. / An ordinal transformation framework for multi-fidelity simulation optimization. IEEE International Conference on Automation Science and Engineering. Vol. 2014-January IEEE Computer Society, 2014. pp. 385-390
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