MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling

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

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.

Original languageEnglish (US)
Article number1650017
JournalAsia-Pacific Journal of Operational Research
Issue number3
StatePublished - Jun 1 2016


  • Multi-fidelity simulation
  • optimal sampling
  • ordinal transformation
  • simulation optimization

ASJC Scopus subject areas

  • Management Science and Operations Research


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