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

Giulia Pedrielli, K. Selcuk Candan, Xilun Chen, Logan Mathesen, Alireza Inanalouganji, Jie Xu, Chun Hung Chen, Loo Hay Lee

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Article number1940011
JournalAsia-Pacific Journal of Operational Research
Issue number6
StatePublished - Dec 1 2019


  • Simulation optimization
  • multi-fidelity
  • ordinal learning
  • real-time decision making
  • stochastic optimization

ASJC Scopus subject areas

  • Management Science and Operations Research


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