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Generalized Ordinal Learning Framework (GOLF) for Decision Making with Future Simulated Data

Author

Listed:
  • Giulia Pedrielli

    (School of Computing Informatics and Decision Systems Engineering Arizona State University, 699 S Mill Ave Tempe, Arizona 85251, USA)

  • K. Selcuk Candan

    (School of Computing Informatics and Decision Systems Engineering Arizona State University, 699 S Mill Ave Tempe, Arizona 85251, USA)

  • Xilun Chen

    (School of Computing Informatics and Decision Systems Engineering Arizona State University, 699 S Mill Ave Tempe, Arizona 85251, USA)

  • Logan Mathesen

    (School of Computing Informatics and Decision Systems Engineering Arizona State University, 699 S Mill Ave Tempe, Arizona 85251, USA)

  • Alireza Inanalouganji

    (School of Computing Informatics and Decision Systems Engineering Arizona State University, 699 S Mill Ave Tempe, Arizona 85251, USA)

  • Jie Xu

    (Systems Engineering and Operations Research Department, George Mason University 4400 University Drive Fairfax, Virginia 22030, USA)

  • Chun-Hung Chen

    (Systems Engineering and Operations Research Department, George Mason University 4400 University Drive Fairfax, Virginia 22030, USA)

  • Loo Hay Lee

    (Department of Industrial Systems Engineering & Management, National University of Singapore 1 Engineering, Drive 2 Singapore 117576, Singapore)

Abstract

Real-time decision making has acquired increasing interest as a means to efficiently operating complex systems. The main challenge in achieving real-time decision making is to understand how to develop next generation optimization procedures that can work efficiently using: (i) real data coming from a large complex dynamical system, (ii) simulation models available that reproduce the system dynamics. While this paper focuses on a different problem with respect to the literature in RL, the methods proposed in this paper can be used as a support in a sequential setting as well. The result of this work is the new Generalized Ordinal Learning Framework (GOLF) that utilizes simulated data interpreting them as low accuracy information to be intelligently collected offline and utilized online once the scenario is revealed to the user. GOLF supports real-time decision making on complex dynamical systems once a specific scenario is realized. We show preliminary results of the proposed techniques that motivate the authors in further pursuing the presented ideas.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:apjorx:v:36:y:2019:i:06:n:s0217595919400116
    DOI: 10.1142/S0217595919400116
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    References listed on IDEAS

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    2. Wang, Tianxiang & Xu, Jie & Hu, Jian-Qiang & Chen, Chun-Hung, 2023. "Efficient estimation of a risk measure requiring two-stage simulation optimization," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1355-1365.

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