Improving non-deterministic uncertainty modelling in Industry 4.0 scheduling

TL;DR

This paper introduces a probabilistic uncertainty modeling approach for Industry 4.0 scheduling that improves decision-making accuracy by effectively handling environmental and human operator uncertainties, validated on real industrial data.

Contribution

It presents a novel, self-adjusting probabilistic model using epsilon-contamination to better quantify non-deterministic uncertainties in industrial scheduling.

Findings

Enhanced scheduling performance demonstrated on real industrial data

Robustness of the model with limited or incomplete data sets

Significant reduction in delays and budget estimation errors

Abstract

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.