TY - JOUR
T1 - From Data to Stochastic Modeling and Decision Making
T2 - What Can We Do Better?
AU - Berkhout, Joost
AU - Heidergott, Bernd
AU - Lam, Henry
AU - Peng, Yijie
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Data analytics
KW - sensitivity analysis
KW - Taylor series expansion
UR - http://www.scopus.com/inward/record.url?scp=85076561389&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076561389&partnerID=8YFLogxK
U2 - 10.1142/S0217595919400128
DO - 10.1142/S0217595919400128
M3 - Article
AN - SCOPUS:85076561389
SN - 0217-5959
VL - 36
SP - 1
EP - 20
JO - Asia-Pacific Journal of Operational Research
JF - Asia-Pacific Journal of Operational Research
IS - 6
M1 - 1940012
ER -