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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Joost Berkhout & Bernd Heidergott & Henry Lam & Yijie Peng, 2019. "From Data to Stochastic Modeling and Decision Making: What Can We Do Better?," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-20, December.
    • Handle: RePEc:wsi:apjorx:v:36:y:2019:i:06:n:s0217595919400128
    DOI: 10.1142/S0217595919400128
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    References listed on IDEAS

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