From Data to Stochastic Modeling and Decision Making: What Can We Do Better?

Joost Berkhout (), Bernd Heidergott, Henry Lam () and Yijie Peng ()
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Joost Berkhout: Department of Mathematics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
Bernd Heidergott: Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
Henry Lam: Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA
Yijie Peng: Department of Industrial Engineering and Management, College of Engineering, Peking Universtiy, Beijing 100871, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2019, vol. 36, issue 06, 1-20

Abstract: In the past decades we have witnessed a paradigm-shift from scarcity of data to abundance of data. Big data and data analytics have fundamentally reshaped many areas including operations research. In this paper, we discuss how to integrate data with the model-based analysis in a controlled way. Specifically, we consider techniques to quantify input uncertainty and the decision making under input uncertainty. Numerical experiments demonstrate that different ways in decision making may lead to significantly different outcomes in a maintenance problem.

Keywords: Data analytics; Taylor series expansion; sensitivity analysis (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:36:y:2019:i:06:n:s0217595919400128

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DOI: 10.1142/S0217595919400128

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