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

Jie Xu (), Si Zhang, Edward Huang (), Chun-Hung Chen (), Loo Hay Lee () and Nurcin Celik ()
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Jie Xu: Department of System Engineering and Operations Research, George Mason University, Fairfax, VA 22030, United States
Si Zhang: Department of Management Science and Engineering, Shanghai University, Shanghai 200444, China
Edward Huang: Department of System Engineering and Operations Research, George Mason University, Fairfax, VA 22030, United States
Chun-Hung Chen: Department of System Engineering and Operations Research, George Mason University, Fairfax, VA 22030, United States
Loo Hay Lee: Department of Industrial and Systems Engineering, The National University of Singapore, Kent Ridge 119260, Singapore
Nurcin Celik: Department of Industrial Engineering, The University of Miami, Coral Gables, FL 33146, USA

Asia-Pacific Journal of Operational Research (APJOR), 2016, vol. 33, issue 03, 1-26

Abstract: 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.

Keywords: Multi-fidelity simulation; simulation optimization; ordinal transformation; optimal sampling (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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DOI: 10.1142/S0217595916500172

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