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

Joost Berkhout, Bernd Heidergott*, Henry Lam, Yijie Peng

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Article number1940012
JournalAsia-Pacific Journal of Operational Research
Volume36
Issue number6
Early online date15 Nov 2019
DOIs
Publication statusPublished - Dec 2019

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Keywords

  • Data analytics
  • sensitivity analysis
  • Taylor series expansion

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