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

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

E-mail Address: gpedriel@asu.edu

Corresponding author.

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K. Selcuk Candan

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

E-mail Address: candan@asu.edu

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Xilun Chen

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

E-mail Address: cupidcxl@gmail.com

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Logan Mathesen

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

E-mail Address: lmathese@asu.edu

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Alireza Inanalouganji

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

E-mail Address: ainanlou@asu.edu

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Jie Xu

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

E-mail Address: jxu13@gmu.edu

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Chun-Hung Chen

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

E-mail Address: cchen9@gmu.edu

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, and

Loo Hay Lee

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

E-mail Address: iseleelh@nus.edu.sg

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https://doi.org/10.1142/S0217595919400116Cited by:16 (Source: Crossref)

This article is part of the issue:

Special Issue — Simulation Analytics: Smart Simulation in Big Data Era

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:

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