Fuzzy Risk Analysis Research Articles

Fuzzy risk analysis in crop selection using information measures on quadripartitioned single-valued neutrosophic sets

Crop selection is a critical task that involves assessing multiple risk factors to achieve maximum yield. Therefore, risk assessment plays a pivotal role in choosing suitable crops for cultivation. Probabilistic and possibilistic methods are the two primary approaches used for the crop selection process. Probabilistic methods are cost-intensive due to data handling, while possibilistic methods, utilizing linguistic variables, are considered more reliable. In this article, we represent such linguistic variables using Quadripartitioned Single-Valued Neutrosophic Sets (QSVNSs), extensions of Single-Valued Neutrosophic Sets (SVNSs) characterized by four independent membership functions that enhance uncertainty accommodation. We propose two QSVNS-based information measures, entropy and similarity, to transform the final risks evaluated in the form of QSVNSs into linguistic variables for easier comprehension by the general audience. We apply this framework to mustard and paddy cultivation in Assam, a state located in northeastern India, conducting thorough analyses and validation tests. Additionally, statistical tests are presented to demonstrate the significance of the obtained results. The final risk of mustard cultivation is found to be ‘absolutely high’, as the largest similarity value of 0.9910 is obtained for the linguistic term ‘absolutely high’ among all computed similarity value results. Similarly, for paddy cultivation, the largest similarity value of 0.9159 is obtained for the linguistic term ‘very low’, indicating its final risk in our assumed scenario. Thus, the veracity and proficiency of our newly constructed measures are reflected in their practical applications.

Ordering Fuzzy Numbers with Weighted Mean Values and Their Application to Multi-Criteria Decision-Making Problem

The most important aspect of fuzzy numbers is their ordering, which ensures a wide range of their applications in professional life and many academic applied models like linguistic decision-making and fuzzy risk analysis. Even though many researchers have presented various methods, there is still a lot of interest and scope for studies to address the weakness of methods. This paper proposes an approach for ordering generalized fuzzy numbers using weighted mean values (centroid values) of the left and the right fuzziness regions and exponential values of the altitude of the fuzzy number. The proposed method can order two or more fuzzy numbers simultaneously, irrespective of their linear or non-linear membership functions. Furthermore, the proposed method consistently orders the symmetrical fuzzy numbers, the partnered image of the fuzzy number, and the fuzzy numbers that depict the compensation of areas. The advantages of the proposed approach are demonstrated through numerical examples with various types of fuzzy numbers and comparisons with the existing techniques published in the literature. Finally, the proposed method is effectively applied to solve a linguistic multi-criteria decision-making problem related to the stock market.

Modeling a Reverse Logistics Supply Chain for End-of-Life Vehicle Recycling Risk Management: A Fuzzy Risk Analysis Approach

The automotive industry is one of the largest consumers of natural resources, and End-of-Life Vehicles (ELVs) form bulky wastes when they reach the end of their useful life, hence environmental concerns. Efficiency in recycling ELVs is therefore becoming a major concern to address the number of ELVs collected and recycled to minimize environmental impacts. This paper seeks to describe several activities of a closed-loop reverse logistics supply chain for the collection and recycling of ELVs and to identify the related potential risks involved. This study further investigated the potential risks for managing the efficient recycling of ELVs by modeling and viewing the end-of-life vehicle (ELV) recycling system as a reverse logistics supply chain. ELV recycling steps and processes, including collection and transportation, as well as the laws and technologies, were analyzed for risk factor identification and analysis. The major aim of this research is to perform a unified hierarchical risk analysis to estimate the degree of risk preference to efficiently manage the ELV supply chain. This study also proposes a risk assessment procedure using fuzzy knowledge representation theory to support ELV risk analysis. As a result, the identified key risks were ranked in terms of their preference for occurrence in a reverse supply chain of ELV products and mapped into five risk zones, Very Low, Low, Medium-Low, Moderate, Serious, and Critical, for ease of visualization. Hence, with a step-by-step implementation of the presented solution, ELV recycling organizations will see benefits in terms of an improvement in their activities and thus reduced costs that may occur due to uncertainties in their overall ELV business.

New Ranking of Generalized Quadrilateral Shape Fuzzy Number Using Centroid Technique

The output of the fuzzy set is reduced by one for the defuzzification procedure. It is employed to provide a comprehensible outcome from a fuzzy inference process. This page provides further information about the defuzzification approach for quadrilateral fuzzy numbers, which may be used to convert them into discrete values. Defuzzification demonstrates how useful fuzzy ranking systems can be. Our major purpose is to develop a new ranking method for generalized quadrilateral fuzzy numbers. The primary objective of the research is to provide a novel approach to the accurate evaluation of various kinds of fuzzy integers. Fuzzy ranking properties are examined. Using the counterexamples of Lee and Chen demonstrates the fallacy of the ranking technique. So, a new approach has been developed for dealing with fuzzy risk analysis, risk management, industrial engineering and optimization, medicine, and artificial intelligence problems: the generalized quadrilateral form fuzzy number utilizing centroid methodology. As you can see, the aforementioned scenarios are all amenable to the solution provided by the generalized quadrilateral shape fuzzy number utilizing centroid methodology. It’s laid out in a straightforward manner that’s easy to grasp for everyone. The rating method is explained in detail, along with numerical examples to illustrate it. Last but not least, stability evaluations clarify why the Generalized quadrilateral shape fuzzy number obtained by the centroid methodology outperforms other ranking methods.

A New Approach of Ranking of Generalized Trapezoidal Fuzzy Numbers and Application in Fuzzy Risk Analysis

In this present work, a new approach for ranking generalized trapezoidal fuzzy numbers has been introduced. This new proposed method is based on a left and right deviation and fuzzyness measure of the generalized trapezoidal fuzzy numbers. Some propositions have been introduced corresponding to the new proposed method. The proposed method has been compared with the different existing methods of ranking generalized trapezoidal fuzzy numbers. Also, it has been shown that the proposed technique succeeds in overcoming the drawbacks of the existing methods. The proposed method has been applied to a risk analysis problem in the selection of a production house by a retailer.

Setback in ranking fuzzy numbers: a study in fuzzy risk analysis in diabetes prediction

Setback in ranking fuzzy numbers: a study in fuzzy risk analysis in diabetes prediction

A Framework for Project Delivery Systems via Hybrid Fuzzy Risk Analysis: Application and Extension in ICT

One of the essential factors of project success is selecting the proper delivery method. This study aimed to provide a new hybrid decision-making framework to assist project stakeholders in evaluating and selecting the most appropriate Project Delivery System (PDS) and documenting the decision process. For this purpose, the selection factors of PDSs were obtained from a literature review, and critical selection factors were screened based on the fuzzy Delphi method, whereby expert feedback was on Information and Communication Technology (ICT) projects was obtained. Subsequently, the ICT project risks were identified and categorized into six competitive constraints, including time, cost, quality, reputation, value, and scope, and the risk factors were prioritized in each area. Then, the effect of project risks on the decision criteria was investigated using a fuzzy cognitive map (FCM). Finally, the PDSs were ranked through Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). This article researched a novel multi-layer decision system combining the FCM and FTOPSIS techniques. The decision criteria received their weights from the evaluation of the causal relationships between PDS selection factors and project risks. Thus, PDSs were ranked based on different project characteristics, the opinions of stakeholders, and the effect of project risks on the decision-making process; this increased the likelihood of project success. The results showed that the impact of the most critical project risks on the selection factors was so severe that they changed the weight of the criteria in the decision matrix and, subsequently, changed the ranking of decision options.

Operational risk analysis in business processes using decomposed fuzzy sets

Risk assessment takes place depending on the expertise and subjective linguistic assessments of experts. Expert judgements are collected via a questionnaire or an interview including qualitative data. Pessimistic or optimistic status of experts can affect their perceptions on risk. Furthermore, expert judgments are affected by questions’ structure based on whether it is a positive type question (e.g., ‘What is the occurrence probability of the accident?) or a negative type question (e.g., ‘What is the non-occurrence probability of the accident?). All of these cases create uncertainties in the risk assessment process. For this reason, there are various studies using fuzzy risk analysis models to address these uncertainties in risk assessment. However, there is not any risk assessment tool that considers the uncertainties caused by the factors mentioned above, simultaneously. Therefore, in this paper, we introduce the concept of decomposed fuzzy sets (DFS) to model human thoughts and perceptions in a more realistic and detailed way through optimistic and pessimistic membership functions. We present the basic operations on decomposed fuzzy sets and their properties. To demonstrate the utility of the proposed method, the method is applied to operational risk analysis in business processes. The data used in the application are collected from the managerial board of a construction company. The application results and advantages of the proposed method are presented together with a comparative analysis.

Bipolar Dissimilarity and Similarity Correlations of Numbers

Many papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity functions (fuzzy relations) defined on a domain with involutive (negation) operation. The bipolarity property reflects a structure of the domain with involutive operation, and bipolar similarity functions are more suitable for calculating a similarity between elements of such domain. On the set of real numbers, similarity measures should take into account symmetry between positive and negative numbers given by involutive negation of numbers. Another reason to consider bipolar similarity functions is that these functions define measures of correlation (association) between elements of the domain. The paper gives a short introduction to the theory of correlation functions defined on sets with an involutive operation. It shows that the dissimilarity function generating Pearson’s correlation coefficient is bipolar. Further, it proposes new normalized similarity and dissimilarity functions on the set of real numbers. It shows that non-bipolar similarity functions have drawbacks in comparison with bipolar similarity functions. For this reason, bipolar similarity measures can be recommended for use in fuzzy risk analysis. Finally, the correlation functions between numbers corresponding to bipolar similarity functions are proposed.

Fuzzy risk analysis based on a similarity measure of fuzzy numbers and its application in crop selection

Crop selection is an important task for optimum output, as it involves various risk factors. Hence, it requires performing risk analysis on crop selection. Generally, these risk factors involved in crop selection can be assessed by probabilistic methods, which require lots of data and are very expensive. As such, an alternative to probabilistic analysis, possibilistic analysis can be performed where the data involved are linguistic variables. These linguistic variables can be observed as fuzzy numbers, in which case fuzzy risk analysis plays an important role. Generally, in the process of fuzzy risk analysis, the final risks are fuzzy numbers, which have to be transformed into linguistic variables, so that the final risks become communicable to the general audience. The similarity measure is such a tool that helps in the process of transformation. Literature reveals lots of similarity measures since its development. This happens as there is not a single universally accepted measure. In this work, an attempt is being made to develop a novel measure of similarity between fuzzy numbers. Generally, the existing measures do not comply with the basic properties of a similarity measure. Nevertheless, the current measure oath to follow some reasonable properties of the similarity measure.

A novel method for ranking generalized fuzzy numbers with two different heights and its application in fuzzy risk analysis

Due to the large use of fuzzy numbers, the ranking of these numbers is very important.In this paper, we propose a new method for ranking generalized fuzzy numbers with different left and right heights.The proposed method, at first obtains the centers of gravity of fuzzy numbers and left and right side crisp numbers; then by computing left and right areas associated with them, ranks the fuzzy numbers. The proposed method can overcome the flaws and defects of some ranking methods, and the provided examples are evidence of this. Finally this method is applied to the fuzzy risk analysis problem.

Fuzzy risk analysis using a new technique of ranking of generalized trapezoidal fuzzy numbers

Fuzzy risk analysis using a new technique of ranking of generalized trapezoidal fuzzy numbers

The Arithmetic Operator of Fuzzy Regular Prismoid Numbers and Its Application to Fuzzy Risk Analysis

The Arithmetic Operator of Fuzzy Regular Prismoid Numbers and Its Application to Fuzzy Risk Analysis

Technology for risk assessment at product lifecycle stages using fuzzy logic

The problem of risk assessment at the stages of the product life cycle using both qualitative and quantitative approaches is investigated, and a generalized algorithm for selecting a fuzzy risk assessment model with different input data and system requirements is proposed for the effective use of statistical information and expert assessments. The "risk-based approach" allows to reduce the cost of correcting possible errors in the future and reduce the uncertainty when performing subsequent actions. It is noted that the results of SWOT analysis, as a rule, are of a qualitative descriptive nature, and do not contain specific recommendations. The provisions of modern standards on risk analysis are analyzed and the classification of risk analysis methods is given in accordance with the provisions of the national standard GOST R 58771-2019 "Risk management. Technologies for risk assessment", in which the key is the concept of uncertainty, estimated using different scales of gradation of risk damage and probability of its occurrence. An approach based on fuzzy logic and a hybrid fuzzy neural network model is proposed, which allows to present the used criteria in a con-venient form and implement a logical conclusion using simple and visual production rules. At the same time, the effectiveness and accuracy of the developed risk assessment system based on fuzzy logic is mainly determined by the quality of expert information and the consistency of the methods used to obtain it. To improve the accuracy of the results, it is proposed to use collective expert estimates with subsequent analysis of the consistency of the obtained expert estimates by determining the coefficients of variation, rank correlation, concordation, and so on. A generalized algorithm of expert assessment is presented, which is recommended to follow when developing expert systems for risk analysis. Various models of fuzzy inference (Mamdani, Takagi-Sugeno, hybrid neuro-fuzzy inference) are considered. An algorithm for constructing a fuzzy risk analysis system based on an effective method for obtaining expert assessments and analyzing statistical information is proposed. It is suggested that if there is a priori information about previously occurred events that can be used for risk analysis and fore casting, the fuzzy conclusion should be refined using widely known methods of mathematical statistics, optimization algorithms, for example, gradient descent, simplex method or genetic algorithms. An example of developing a risk assessment system when an enterprise enters into contracts with both the customer and co-executors is given.

Model for fuzzy risk analysis applied in complex projects

Model for fuzzy risk analysis applied in complex projects

Fuzzy risk analysis of dam overtopping from snowmelt floods in the nonstationarity case of the Manas River catchment, China

With the significant climate change that has occurred in the Manas River catchment, the temporal and spatial patterns of the natural changes in the regional water cycle have changed dramatically in the past 30 years, and the frequency of extreme hydrological events has increased, which has changed the overall stability of the hydrological system of the catchment. According to the annual maximum peak flow data for the period 1956–2014 in the Manas River catchment, we used variable fuzzy set theory and the Mann–Kendall test to conduct trend and change point tests, respectively, and decomposition synthesis theory was used to handle consistency correction. Combined with the flood routing results for the Kensiwate reservoir using Monte Carlo simulation, a reservoir overtopping risk model based on right-angle trapezoidal fuzzy numbers was established, and the fuzzy risk index intervals and the corresponding fuzzy risk rate intervals for the Kensiwate reservoir were considered for past and present conditions. The results show that the local tendency and jumping of the maximum flood peak series showed significant changes; the annual runoff had an obvious growth trend in the period 1985–2006 and a gradually varying qualitative change from 1975 to 2000. In particular, the characteristic parameter reached an extreme qualitative change level of − 0.0016 in 1993. Then, we used decomposition synthesis theory to process the sequence with jumping points and obtained the past and present hydrological time series. Under the present conditions, the average flood peak sequence is 58.10% higher than in the past, and the flood design value decreases with increasing design frequency; however, when the present sequence increases, the change rates of both flood design values increase. The reservoir overtopping risk increased under the current conditions of snowmelt floods, which was affected by a significant increase in catchment temperature. Moreover, the use of right-angle trapezoidal fuzzy numbers to describe the reservoir overtopping risk was more in line with the objective reality than a traditional triangular fuzzy number. The results of this study can be used for the efficient utilization of water resources in the Manas River catchment and provide a new reference for the scientific management of reservoirs.

An Interval Type-2 Fuzzy Risk Analysis Model (IT2FRAM) for Determining Construction Project Contingency Reserve

Determining contingency reserve is critical to project risk management. Classic methods of determining contingency reserve significantly rely on historical data and fail to effectively incorporate certain types of uncertainties such as vagueness, ambiguity, and subjectivity. In this paper, an interval type-2 fuzzy risk analysis model (IT2FRAM) is introduced in order to determine the contingency reserve. In IT2FRAM, the membership functions for the linguistic terms used to describe the probability, impact of risk and the opportunity events are developed, optimized, and aggregated using interval type-2 fuzzy sets and the principle of justifiable granularity. IT2FRAM is an extension of a fuzzy arithmetic-based risk analysis method which considers such uncertainties and addresses the limitations of probabilistic and deterministic techniques of contingency determination methods. The contribution of IT2FRAM is that it considers the opinions of several subject matter experts to develop the membership functions of linguistic terms. Moreover, the effect of outlier opinions in developing the membership functions of linguistic terms are reduced. IT2FRAM also enables the aggregation of non-linear membership functions into trapezoidal membership functions. A hypothetical case study is presented in order to illustrate the application of IT2FRAM in Fuzzy Risk Analyzer© (FRA©), a risk analysis software.

Evaluation and selection of construction projects based on risk analysis

The project selection problem has attracted significant attention among construction project participants. Typically, a contractor can execute one or more projects from available projects. This study introduces a new strategy to help contractors in evaluating and selecting suitable construction projects. The new strategy is based on identifying multiple criteria that affect construction projects as well as developing and using a model that supports decision based on the analytic hierarchy process. Furthermore, a fuzzy risk analysis model is proposed to analyze and quantify some of the identified criteria to help contractors select minimally risky projects. Market conditions, financial resources, project conditions, time delays, and cost overruns are five identified criteria that control the selection. Through identifying 70 subsidiary factors, the effects and importance of the first three criteria were determined through a field survey, while time delay and cost overrun criteria were analyzed and quantified through the risk analysis model. The new strategy was applied on a real case study comprising two HVAC system projects in the construction industry (project 1 in Egypt and project 2 in Saudi Arabia). Criteria and causes that yield different effects on the new strategy to support contractor decision and selection control are discussed in detail. Risk factors were ranked according to their importance, and their effects on both time and cost were clarified. The results obtained from the investigated case study showed 55.5% support for project 2, and only 44.5% support for project 1. This strategy is applicable to other case studies in different countries with different projects, criteria, and execution activities.

A new approach to similarity measure for generalized trapezoidal fuzzy numbers and its application to fuzzy risk analysis

A new similarity measure has been proposed in this paper for generalized trapezoidal fuzzy numbers (GTrFNs). Here, the proposed similarity measure has been devised based on exponent distance, area, perimeter and height of a GTrFN. Most of the researchers described the similarity measure between two GTrFNs whose components belong to [0, 1] only. But the components of GTrFNs should be any real numbers belonged to R. For this reason, in this paper a similarity measure technique has been framed newly on GTrFN whose components are any real number which is the most important consideration here. Depending on the proposed method of similarity measure, some essential properties have been illustrated in this paper. Also, this method has been compared with some existing techniques of similarity measure taking twenty five different sets of GTrFNs. Henceforth, it is obtained that our proposed technique is better than other existing techniques. Finally, the proposed similarity measure has been applied in fuzzy risk analysis in a production system which has been described with numerical illustration.

Adapted a novel similarity and its application in fuzzy risk analysis

Generalized trapezoidal fuzzy numbers (GTFNs) and their similarity measures have been widely applied in fuzzy risk analysis. However, some existing similarity measures of GTFNs cannot identify the similarity of some special GTFNs properly. In this study, we introduce the exponential distance of center of gravity (COG) and an amending value to adapted a novel similarity measure between GTFNs. Then, seven properties of this new method are investigated and proved. In addition, in order to verify the superiority of the new method, fifteen special testing sets are given to compare the performance of seven existing similarity measures with the new method. Moreover, the presented new similarity measure is used to solve a case study about the failure risk analysis of six major subsystems of the reciprocating pump system (RPS) in which different parameters are expressed by generalized trapezoidal fuzzy linguistic term.