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

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.

Quantitative risk management in gas injection project: a case study from Oman oil and gas industry

“…It is a universal fact that there is no project without uncertain events. Under uncertainty, we are confronted with the lack of data and information such that deterministic approach cannot truly calculate the risk involved (Khalaj et al 2013). Also, it has been observed that many organizations refrain from applying quantitative risk analysis method due to the lack of knowledge of using it and the benefits such an analysis can add to their projects.…”

Robust DEA under discrete uncertain data: a case study of Iranian electricity distribution companies

“…In the real-world problems, data are often contaminated by perturbations (Izadi and Kimiagari 2014;Khalaj et al 2013;Hosseini and Tarokh 2011;Shad et al 2014). Because of perturbations in data, the efficient frontier in DEA is changed and the determined targets may become incorrect.…”

Microstructure and Mechanical Characterization of X70 Steel Welded Joints Through Hardness Mapping and Tensile Strength Testing

“…Among the wide range of pipeline steels, API X70 is one of the most important steels extensively applied in oil and gas industries [5]. However, during fusion welding processes, the mechanical properties of X70 steel, especially in heat affected zone (HAZ), sharply deteriorate after experiencing a severe welding thermal cycle [6][7][8]. Therefore, a widespread interest has been taken by researchers to study joining process of X70 steel and optimize strength and toughness of the weld metal (WM) and the HAZ.…”