Method For Fuzzy Risk Analysis Research Articles

Fuzzy risk analysis under influence of non-homogeneous preferences elicitation in fiber industry

Fuzzy risk analysis plays an important role in mitigating the levels of harm of a risk. In real world scenarios, it is a big challenge for risk analysts to make a proper and comprehensive decision when coping with risks that are incomplete, vague and fuzzy. Many established fuzzy risk analysis approaches do not have the flexibility to deal with knowledge in the form of preferences elicitation which lead to incorrect risk decision. The inefficiency is reflected when they consider only risk analyst preferences elicitation that is partially known. Nonetheless, the preferences elicited by the risk analyst are often non-homogeneous in nature such that they can be completely known, completely unknown, partially known and partially unknown. In this case, established fuzzy risk analysis methods are considered as inefficient in handling risk, hence an appropriate fuzzy risk analysis method that can deal with the non-homogeneous nature of risk analyst’s preferences elicitation is worth developing. Therefore, this paper proposes a novel fuzzy risk analysis method that is capable to deal with the non-homogeneous risk analyst’s preferences elicitation based on grey numbers. The proposed method aims at resolving the uncertain interactions between homogeneous and non-homogeneous natures of risk analyst’s preferences elicitation by using a novel consensus reaching approach that involves transformation of grey numbers into grey parametric fuzzy numbers. Later on, a novel fuzzy risk assessment score approach is presented to correctly evaluate and distinguish the levels of harm of the risks faced, such that these evaluations are consistent with preferences elicitation of the risk analyst. A real world risk analysis problem in fiber industry is then carried out to demonstrate the novelty, validity and feasibility of the proposed method.

FUZZY RISK ANALYSIS BASED ON A NEW METHOD FOR RANKING GENERALIZED FUZZY NUMBERS

Fuzzy risk analysis, as a powerful tool to address uncertain information, can provide an appropriate method for risk analysis. However, the previous fuzzy risk analysis methods still have some weaknesses. To overcome the weaknesses of existing fuzzy risk analysis methods, a novel method for ranking generalized fuzzy numbers is proposed for addressing fuzzy risk analysis problems. In the proposed method, a new value of ranking score is obtained based on ordered weighted averaging (OWA) operator. The proposed method takes into consideration of the different importance of the three scoring factors defuzzified value, height and spread. Comparing to some existing methods, the new method can get more reasonable results in some situations.

A Sustainable Industry-Environment Model for the Identification of Urban Environmental Risk to Confront Air Pollution in Beijing, China

In this study, an optimized industry-environment model is proposed for identifying environmental risk under uncertainties. The strategy associated with an emission-permit trading mechanism has been introduced into the industrial-environment regulation (model) for remitting the pressures of frequent/severe haze events in Beijing City. A dual stochastic mixed fuzzy risk analysis method with Laplace’s criterion (DSFRL) can be embedded into industry-environment issues with a trading emission-permit trading mechanism (IEST) for handling uncertainties regarded as possibility and probability distributions. Meanwhile, this can also reflect the environmental risks and corresponding system benefits due to the occurrence of a random event (such as random wind velocity). Based on the application of the proposed IEST with DSFRL, the numbers of the obtained results associated with production reduction, adjustment of industrial layout pattern, emission-permit transactions, pollutant mitigation and system benefits under various Laplace criterion cases can be analyzed. A tradeoff between production development and pollution mitigation based on the preference of policymakers can be used for rectifying current strategies with a sustainable mode, which can prompt an effort to confront air pollution

Fuzzy risk analysis by similarity-based multi-criteria approach to classify alternatives

In this paper, a new method for fuzzy risk analysis based on similarity of fuzzy numbers is presented. First of all, the similarity-based multi-criteria approach is used to find the similarity between generalized trapezoidal fuzzy numbers and the desired fuzzy number to rank them. The geometry distance, spread difference, perimeter ratio, area ratio, and height ratio have been considered as criteria. Secondly, the proposed ranking approach is used for fuzzy risk analysis. Severity of loss, probability of failure, and failure ignorance possibility are the parameters to assess the risk of various alternatives. In the proposed fuzzy risk analysis, the alternatives can be classified into different levels in terms of their risk. Therefore, fuzzy risk analysis leads to classification and prioritization of the alternatives. Finally, a numerical example is presented to illustrate the applicability of the proposed method.

A new similarity measure for interval type-2 fuzzy sets: Application in fuzzy risk analysis

Here, we present a fuzzy risk analysis method based on a new similarity measure of interval type-2 fuzzy sets (IT2-FSs). First, we present a new similarity measure for IT2-FSs. This measure considers features of IT2-FSs such as cardinality, height and distance. We establish some properties of the proposed similarity measure. We make an experiment to compare the experimental results obtained by the proposed method with the ones obtained by other existing similarity measures. The proposed method shows not to have the drawbacks of the existing methods. Applying the proposed method, we develop a fuzzy risk analysis approach extending the utility of fuzzy risk analysis methods in dealing with sophisticated problems. The factors used in the fuzzy risk analysis are the probability of failure, severity of loss, probability of failure detection and economic disbenefits of failure occurrence, features not being possible to use in existing interval type-2 fuzzy risk analysis methods.

Fuzzy risk analysis based on the novel fuzzy ranking with new arithmetic operations of linguistic fuzzy numbers

The project of managing risk analysis problems plays an important task in present world. The arithmetic operations of some fuzzy risk analysis methods do not satisfy some properties of linguistic fuzzy numbers and the comparison of the trapezoidal fuzzy numbers does not coincide with the perception of the individual. In this paper, we present a new ranking on generalized fuzzy numbers and new arithmetic operations such as fuzzy addition, fuzzy multiplication, and fuzzy division on linguistic fuzzy numbers. Also, we present an efficient approach for fuzzy risk analysis based on new arithmetic operators of the linguistic trapezoidal fuzzy numbers and new fuzzy ranking.

The Interval-Valued Trapezoidal Approximation of Interval-Valued Fuzzy Numbers and Its Application in Fuzzy Risk Analysis

Taking into account that interval-valued fuzzy numbers can provide more flexibility to represent the imprecise information and interval-valued trapezoidal fuzzy numbers are widely used in practice, this paper devotes to seek an approximation operator that produces an interval-valued trapezoidal fuzzy number which is the nearest one to the given interval-valued fuzzy number, and the approximation operator preserves the core of the original interval-valued fuzzy number with respect to the weighted distance. As an application, we use the interval-valued trapezoidal approximation to handle fuzzy risk analysis problems, which overcome the drawback of existing fuzzy risk analysis methods.

Fuzzy risk analysis based on ranking generalized fuzzy numbers with different left heights and right heights

In this paper, we present a new method for fuzzy risk analysis based on the proposed new fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. First, we present a fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. The proposed method considers the areas of the positive side, the areas of the negative side and the centroid values of generalized fuzzy numbers as the factors for calculating the ranking scores of generalized fuzzy numbers with different left heights and right heights. It can overcome the drawbacks of the existing fuzzy ranking methods. Then, we propose a new method for fuzzy risk analysis based on the proposed fuzzy ranking method, where the evaluating values are represented by generalized fuzzy numbers. The proposed fuzzy risk analysis method provides us with a useful way for fuzzy risk analysis based on generalized fuzzy numbers with different left heights and right heights.

An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers

This paper presents a novel method of fuzzy risk analysis based on a new similarity measure of generalized fuzzy numbers. This similarity measure considers many features of generalized fuzzy numbers such as the area, perimeter, height and geometric distance of these kinds of fuzzy numbers. Using some sets of generalized fuzzy numbers, we show the power of this similarity measurement method to overcome the drawbacks that other methods are suffering. Applying the proposed method, we present an improved fuzzy risk analysis method which develops the capability of fuzzy risk analysis methods to deal with sophisticated problems. In the proposed method we use new factors such as probability of failure detection and economic disbenefits of failure occurrence which have not been used in fuzzy risk analysis methods before.

A New Fuzzy Risk Analysis Method based on Generalized Fuzzy Numbers

Risk analysis plays an important role in many application systems. The current researches prefer to use fuzzy set theory for risk analysis. In this paper, we present a new fuzzy risk analysis method based on generalized fuzzy numbers. Firstly, we define new arithmetic operations between generalized fuzzy numbers. Then, we propose a new method to measure the degree of similarity between generalized fuzzy numbers. Finally, we apply the new arithmetic operations between generalized fuzzy numbers and proposed similarity measure to develop a new method to deal with fuzzy risk analysis problems. The greatest advantage of the new method is that it has less computational complexity. When dealing with the risk analysis problems, the predominance of new method has been showed: easier and more useful.

A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers

At present, some researchers provide a type of fuzzy risk analysis algorithms for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by trapezoidal fuzzy numbers. In those algorithms, the main operations are two: one is arithmetic operators of the trapezoidal fuzzy numbers; the other is the similarity of the trapezoidal fuzzy numbers. However, the arithmetic operators of some algorithms do not satisfy some properties of the trapezoidal fuzzy numbers and the similarity of the trapezoidal fuzzy numbers does not coincide with the intuition of the human being. Due to this situation, in this paper, we present an efficient approach for fuzzy risk analysis based on some new arithmetic operators of the trapezoidal fuzzy numbers and propose a new similarity of the trapezoidal fuzzy numbers to deal with fuzzy risk analysis problems. At the same time, we make an experiment to use 30 sets of trapezoidal fuzzy numbers to compare the experimental results of our proposed approach with the existing similarity measures. At last, we use an example to illustrate the efficiency of the new approach.

Fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers and interval-valued fuzzy number arithmetic operators

In this paper, we present a new method for fuzzy risk analysis based on a new similarity measure between interval-valued fuzzy numbers and new interval-valued fuzzy number arithmetic operators. First, we present a new similarity measure between interval-valued fuzzy numbers. The proposed similarity measure considers the similarity of the gravities on the X-axis between upper fuzzy numbers, the difference of the spreads between upper fuzzy numbers, the heights of the upper fuzzy numbers, the degree of similarity on the X-axis between interval-valued fuzzy numbers, and the gravities on the Y-axis between interval-valued fuzzy numbers. We also present three properties of the proposed similarity measure between interval-valued fuzzy numbers. Then, we present new interval-valued fuzzy number arithmetic operators. Finally, we apply the proposed similarity measure between interval-valued fuzzy numbers and the proposed interval-valued fuzzy number arithmetic operators to propose a fuzzy risk analysis algorithm to deal with fuzzy risk analysis problems. The proposed method provides a useful way for handling fuzzy risk analysis problems based on interval-valued fuzzy numbers.

Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads

In this paper, we present a new method for fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. First, we present a new method for ranking generalized fuzzy numbers. The proposed method considers the defuzzified values, the heights and the spreads for ranking generalized fuzzy numbers. Based on the proposed method for ranking generalized fuzzy numbers, we propose a fuzzy risk analysis algorithm to deal with fuzzy risk analysis problems. The proposed method provides a useful way for handling the fuzzy risk analysis problems.

Fuzzy risk analysis based on ranking fuzzy numbers using [formula omitted]-cuts, belief features and signal/noise ratios

In this paper, we present a new approach for fuzzy risk analysis based on the ranking of fuzzy numbers. First, we propose a new method for ranking fuzzy numbers using the α-cuts, the belief feature and the signal/noise ratios, where α ∈ [0, 1]. The proposed method for ranking fuzzy numbers calculates the signal/noise ratio of each α-cut of a fuzzy number to evaluate the quantity and the quality of a fuzzy number, where the signal and the noise are defined as the middle-point and the spread of each α-cut of a fuzzy number, respectively. We use the value of α as the weight of the signal/noise ratio of each α-cut of a fuzzy number to calculate the ranking index of each fuzzy number. The proposed method can rank any kinds of fuzzy numbers with different kinds of membership functions. Then, we apply the proposed fuzzy ranking method to propose a fuzzy risk analysis algorithm to deal with fuzzy risk analysis problems. Because the proposed fuzzy risk analysis method considers the degrees of confidence of decision makers’ opinions, it is more flexible than the existing methods.

Fuzzy risk analysis based on interval-valued fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers. First, we present a new similarity measure between interval-valued fuzzy numbers. It combines the concepts of geometric distance, the perimeter, the height and the center-of-gravity-points of interval-valued fuzzy numbers for calculating the degree of similarity between interval-valued fuzzy numbers. We also prove some properties of the proposed similarity measure. We make an experiment to use nine sets of interval-valued fuzzy numbers to compare the experimental results of the proposed method with the existing similarity measures. The proposed method can overcome the drawbacks of the existing similarity measures. We also propose a new division operator and an interval-valued fuzzy number adjustment algorithm. Based on the proposed similarity measure, new division operator and adjustment algorithm, we present a new fuzzy risk analysis algorithm for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by interval-valued fuzzy numbers. The proposed method provides a useful way to deal with fuzzy risk analysis problems.

A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on similarity measures between generalized fuzzy numbers. First, we present a new similarity measure between generalized fuzzy numbers. It combines the concepts of geometric distance, the perimeter and the height of generalized fuzzy numbers for calculating the degree of similarity between generalized fuzzy numbers. We also prove some properties of the proposed similarity measure. We make an experiment to use 15 sets of generalized fuzzy numbers to compare the experimental results of the proposed method with the existing similarity measures. The proposed method can overcome the drawbacks of the existing similarity measures. Based on the proposed similarity measure between generalized fuzzy numbers, we present a new fuzzy risk analysis algorithm for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by generalized fuzzy numbers. The proposed method provides a useful way to deal with fuzzy risk analysis problems.

Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations

In this paper, we present a new method for fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. First, we present a new method for ranking trapezoidal fuzzy numbers based on their shapes and deviations. Then, we use some examples to compare the proposed method with the existing methods for ranking fuzzy numbers. Finally, we use the proposed fuzzy ranking method to present a new fuzzy risk analysis algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm is more flexible and simpler than the existing methods due to the fact that it allows the evaluating values to be represented by trapezoidal fuzzy numbers with different shapes and different deviations.

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7].

Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Firstly, we present a method called the simple center of gravity method (SCGM) to calculate the center-of-gravity (COG) points of generalized fuzzy numbers. Then, we use the SCGM to propose a new method to measure the degree of similarity between generalized fuzzy numbers. The proposed similarity measure uses the SCGM to calculate the COG points of trapezoidal or triangular generalized fuzzy numbers and then to calculate the degree of similarity between generalized fuzzy numbers. We also prove some properties of the proposed similarity measure and use an example to compare the proposed method with the existing similarity measures. The proposed similarity measure can overcome the drawbacks of the existing methods. We also apply the proposed similarity measure to develop a new method to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis method is more flexible and more intelligent than the existing methods due to the fact that it considers the degrees of confidence of decisionmakers' opinions.