  The latest Industrial revolution has helped industries in achieving very high
rates of productivity and efficiency. It has introduced data aggregation and
cyber-physical systems to optimize planning and scheduling. Although,
uncertainty in the environment and the imprecise nature of human operators are
not accurately considered for into the decision making process. This leads to
delays in consignments and imprecise budget estimations. This widespread
practice in the industrial models is flawed and requires rectification. Various
other articles have approached to solve this problem through stochastic or
fuzzy set model methods. This paper presents a comprehensive method to
logically and realistically quantify the non-deterministic uncertainty through
probabilistic uncertainty modelling. This method is applicable on virtually all
Industrial data sets, as the model is self adjusting and uses
epsilon-contamination to cater to limited or incomplete data sets. The results
are numerically validated through an Industrial data set in Flanders, Belgium.
The data driven results achieved through this robust scheduling method
illustrate the improvement in performance.
