A novel risk-based analysis for the production system under epistemic uncertainty

Mehran Khalaj,Fereshteh Khalaj,Amineh Khalaj

doi:10.1186/2251-712x-9-35

Abstract

Abstract Risk analysis of production system, while the actual and appropriate data is not available, will cause wrong system parameters prediction and wrong decision making. In uncertainty condition, there are no appropriate measures for decision making. In epistemic uncertainty, we are confronted by the lack of data. Therefore, in calculating the system risk, we encounter vagueness that we have to use more methods that are efficient in decision making. In this research, using Dempster-Shafer method and risk assessment diagram, the researchers have achieved a better method of calculating tools failure risk. Traditional statistical methods for recognizing and evaluating systems are not always appropriate, especially when enough data is not available. The goal of this research was to present a more modern and applied method in real world organizations. The findings of this research were used in a case study, and an appropriate framework and constraint for tools risk were provided. The research has presented a hopeful concept for the calculation of production systems' risk, and its results show that in uncertainty condition or in case of the lack of knowledge, the selection of an appropriate method will facilitate the decision-making process.

Highlights

Over the last few decades, different methods of decision making in uncertainty condition have been considered
The different type of available information calls for the development of a different method to represent and propagate the associated uncertainty
In the last decades, Bayesian inferences (Bayes 1763) based on previous applications are valid, but the Dempster-Shafer studies as techniques of modeling in uncertainty condition have had a lot of applications; various perspectives for uncertainty management have been proposed

Summary

Introduction

Over the last few decades, different methods of decision making in uncertainty condition have been considered. Evidence theory which is called Dempster-Shafer theory (DST) introduces a stronger framework for our incomplete knowledge presentation and expression. The different type of available information calls for the development of a different method to represent and propagate the associated uncertainty. The use of evidence theory started with Dempster’s work by description of the accounting principles of the upper and lower probabilities, and mathematical theory of evidence was defined precisely by Shafer (1976). In the last decades, Bayesian inferences (Bayes 1763) based on previous applications are valid, but the Dempster-Shafer studies as techniques of modeling in uncertainty condition have had a lot of applications; various perspectives for uncertainty management have been proposed.

Objectives

Conclusion