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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jie Xu & Si Zhang & Edward Huang & Chun-Hung Chen & Loo Hay Lee & Nurcin Celik, 2016. "MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-26, June.
    • Handle: RePEc:wsi:apjorx:v:33:y:2016:i:03:n:s0217595916500172
    DOI: 10.1142/S0217595916500172
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    References listed on IDEAS

    as
    1. Jie Xu & Barry L. Nelson & L. Jeff Hong, 2013. "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 133-146, February.
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    Cited by:

    1. Darville, Joshua & Yavuz, Abdurrahman & Runsewe, Temitope & Celik, Nurcin, 2023. "Effective sampling for drift mitigation in machine learning using scenario selection: A microgrid case study," Applied Energy, Elsevier, vol. 341(C).
    2. Li, Na & Zhang, Yue & Teng, De & Kong, Nan, 2021. "Pareto optimization for control agreement in patient referral coordination," Omega, Elsevier, vol. 101(C).
    3. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.
    4. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
    5. Jianpei Wen & Hanyu Jiang & Jie Song, 2019. "A Stochastic Queueing Model for Capacity Allocation in the Hierarchical Healthcare Delivery System," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(01), pages 1-24, February.
    6. Wai Kin Victor Chan, 2016. "Linear Programming Formulation of Idle Times for Single-Server Discrete-Event Simulation Models," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(05), pages 1-17, October.
    7. Lingxuan Liu & Leyuan Shi, 2019. "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(05), pages 1-26, October.
    8. Giulia Pedrielli & K. Selcuk Candan & Xilun Chen & Logan Mathesen & Alireza Inanalouganji & Jie Xu & Chun-Hung Chen & Loo Hay Lee, 2019. "Generalized Ordinal Learning Framework (GOLF) for Decision Making with Future Simulated Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-35, December.
    9. Michael Perry & Hadi El-Amine, 2019. "Computational Efficiency in Multivariate Adversarial Risk Analysis Models," Decision Analysis, INFORMS, vol. 16(4), pages 314-332, December.
    10. Robert Cuckler & Kuo-Hao Chang & Liam Y. Hsieh, 2017. "Optimal Parallel Machine Allocation Problem in IC Packaging Using IC-PSO: An Empirical Study," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(06), pages 1-20, December.
    11. Pai Liu & Xi Zhang & Zhongshun Shi & Zewen Huang, 2017. "Simulation Optimization for MRO Systems Operations," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-23, April.

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