Decision dependent stochastic processes

Decision dependent stochastic processes

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Article ID: iaor201527210
Volume: 234
Issue: 3
Start Page Number: 731
End Page Number: 742
Publication Date: May 2014
Journal: European Journal of Operational Research
Authors: , , ,
Keywords: simulation: applications, stochastic processes
Abstract:

Managers, typically, are unaware of the significant impact their decisions could have on the random mechanism driving a data generating process. Here, a new parametric Bayesian technique is introduced that would allow managers to obtain an estimate of the impact of their decisions on the stochastic process driving the data; this, in turn, should enhance a company’s overall decision‐making capabilities. This general approach to modeling decision‐dependency is carried out via an efficient Markov chain Monte Carlo method. A simulated example, and a real‐life example, using historical maintenance and failure time data from a system at the South Texas Project Nuclear Operating Company, exemplifies the paper’s theoretical contributions. Conclusive evidence of decision dependence in the failure time distribution is reported, which in turn points to an optimal maintenance policy that results in potentially large financial savings to the Texas‐based company.

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