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 () and Loo Hay Lee ()
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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

Asia-Pacific Journal of Operational Research (APJOR), 2019, vol. 36, issue 06, 1-35

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.

Keywords: Simulation optimization; multi-fidelity; ordinal learning; stochastic optimization; real-time decision making (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0217595919400116

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