This study focuses on identifying the relationship between risk factors related to environmental conditions, external factors, and technical aspects that affect the cost performance of underwater pipeline construction projects. The main objectives of this study are to understand the qualitative risk assessment that affects the contingency cost performance of underwater pipeline construction projects, and to identify the relationship between various risk factors. The methodology in this study involves a case study approach on a submarine pipeline project in the Java Sea, utilizing historical analysis and expert information to identify potential risks and their impacts on project costs using fuzzy-based qualitative and quantitative analysis. The study found two field condition risk factors, one external risk and thirteen technical risk factors in a submarine pipeline construction project using the S-Lay method. In an academic context, this study conducts an in-depth and detailed analysis of the identification stage, analysis, classification of risk levels (field conditions, external and technical), relationships between risk variables, corrective actions, corrective action costs, and contingency cost analysis. The results of the analysis help in estimating project costs in more detail.

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

Spherical fuzzy sets are a broader type of fuzzy sets that have the ability to handle various scenarios using their membership, non-membership, and neutral membership grades. These sets require that the total of the squares of these grades be no greater than one. This condition extends the possible values for the three grades and enables decision makers to have a wider range of options when assessing a situation. In solving real life problems, it is necessary to describe a real number as a spherical fuzzy set to incorporate the fuzziness, thus, the need to use trapezoidal spherical fuzzy numbers (TSFN). In this paper, the membership functions of the TSFN, their arithmetic operations and their properties are discussed. Also, a ranking function is proposed to order the TSFNs. All these are used to solve a fuzzy risk analysis problem whose parameters are presented as TSFNs.

The employers should be creating a safe workplace environment in working life. A safe working environment is one where risks are eliminated or at an acceptable level. Building works is one of the sectors with the highest number of occupational accidents. In this study, fuzzy logic is proposed to determine the risk levels with linguistic words in risk analysis, which is the most important step of the occupational health management system in the building works. In the fuzzy risk assessment, the fuzzy model was first proposed and then the risk numbers were calculated. In the research, a risk assessment was carried out using fuzzy logic method in a construction site consisting of ten blocks and twelve-storey apartments belonging to a building company. In the fuzzy risk assessment, the fuzzification of the input data, the creation of the member functions of the input and output values, and the fuzzification processes were done with the help of the fuzzy logic toolbox of the MATLAB software program. The results showed that fuzzy risk analysis is effective and credible for creating a safe building site.

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.

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.

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.

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

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.

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.

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

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

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

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

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.

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.