Informatica

This paper firstly distinguishes between risk and Risk Factor (RF), as fundamental concepts in risk management. The need for the current study lies in the fact that although there are various risk analysis methods, but there is no well-defined frameworks for RF analysis. Manifestly, most of the researches on RF have concentrated on identifying them, expressing relationships among them, and descriptive discussion on their root causes, rather than numerically analysing them. Additionally, most of the risk-related methods (e.g. the classical Failure Mode and Effects Analysis (FMEA)) are very sensitive to nuanced changes on inputs. Thus, the paper is to design a robust RF analysis method. The paper proposes a new method called Risk Influential Factor Analysis (RIFA) for evaluation of RFs. Additionally, for getting reliable results, weighting RFs is done by a novel idea named Weight Estimation by Steady Trend (WEST). The WEST is established on the basis of a well-known branch of Multiple Attribute Decision-Making (MADM), called surrogate weighting. Therewith, in analysis section, the paper shows that (I) employing the suggested hybrid RIFA-WEST methodology leads to robust results, (II) compared to the classical FMEA, the RIFA has not many drawbacks to it, and (III) the WEST is comparable with many of the existing surrogate weighting methods. The proposed methodology is applied in a case study from one of the upstream oil engineering disciplines, i.e. Chemical Enhanced Oil Recovery (CEOR).

One of the most recent innovations in the field of fuzzy sets has been continuous intuitionistic fuzzy sets (CINFUSs), where membership and non-membership degrees are defined by nonlinear functions, as a direct extension of intuitionistic fuzzy sets (IFSs). The membership and non-membership degrees of CINFUSs can account for uncertainty at every point since they are represented by continuous structures that change based on how the decision-maker responds to uncertainty. On the other hand, Pythagorean fuzzy sets (PFSs) allow for a more accurate representation of the data and a better way to handle uncertainty in decision issues by reflecting the hesitations of decision-makers over a larger range. Due to these superior advantages of CINFUSs and the fact that PFSs are more comprehensive than IFSs, in this study, continuous Pythagorean fuzzy sets (CPFUSs) have been aimed at introducing to define uncertainty more broadly and accurately by representing PFSs with a continuous structure as in IFSs. In this study, firstly, the basic principles and mathematical operators of CPFUSs have been developed and presented. Then, multi-attribute decision-making (MADM) models have been developed by considering different aggregation operators to indicate the feasibility and effectiveness of the continuous Pythagorean fuzzy (CPFU) extension. The developed CPFU-MADM models have been implemented to the solution of two different decision problems: green supplier selection and waste disposal site selection problems. In addition, sensitivity analyses have been conducted on criterion weights, expert weights and parameters in order to demonstrate the reliability and stability of the developed models. Furthermore, the validity and superiority of the developed models have been indicated by the comparative analysis conducted with IFSs and PFSs-based MADM models in the literature. MADM applications have shown that continuous Pythagorean fuzzy sets can successfully represent the expert decisions with different attitudes within the same model. It has been observed that the rankings of alternatives according to aggregation operators do not change even when there are differences in the score values of the alternatives.

In this study, we evaluated the effects of the normalization procedures on decision outcomes of a given MADM method. For this aim, using the weights of a number of attributes calculated from FAHP method, we applied TOPSIS method to evaluate the financial performances of 13 Turkish deposit banks. In doing this, we used the most popular four normalization procedures. Our study revealed that vector normalization procedure, which is mostly used in the TOPSIS method by default, generated the most consistent results. Among the linear normalization procedures, max-min and max methods appeared as the possible alternatives to the vector normalization procedure.