Risk Analysis Problems Research Articles

ALGORITHM OF COMPREHENSIVE ECONOMIC AND FINANCIAL ANALYSIS TAKING INTO ACCOUNT THE LEVEL OF RISK TOLERANCE OF THE ENTERPRISE

The paper focuses on the analysis of economic activity and financial condition of the enterprise, because it is one of the key management functions. High-quality economic and financial analysis allows the management of the enterprise to make balanced, reasonable management decisions, ensure the efficient use of resources and the achievement of strategic business development goals. The study identified the stages of the historical development of complex economic and financial analysis, presents the features of modern complex economic and financial analysis, indicates the shortcomings, and also developed the author's algorithm for complex economic and financial analysis at the enterprise, taking into account the risk tolerance of the enterprise. This paper presents an algorithm for complex economic and financial analysis that takes into account the level of risk tolerance of the enterprise. The relevance of the study is due to the need to adapt companies to modern conditions of an unstable market and the increasing importance of risk management, which is the key to successful functioning and increased competitiveness. The use of the proposed algorithm will allow the enterprise to make more accurate and sound management decisions, effectively use resources, monitor risks and increase its financial stability. It is important to note that this work focuses on the integration of risk analysis into the overall system of economic and financial analysis, which contributes to the formation of adaptive strategies that ensure sustainable development in a dynamic economic environment. The work is of interest both to theorists seeking to study the problems of risk analysis in more depth, and to practitioners who can use the proposed methods and approaches to improve the performance of their enterprises.

Dynamic human error risk assessment of group decision-making in extreme cooperative scenario

For groups in extreme environments, their reliability requirements in complex operational systems are high due to isolation from society, physical constraints for extended periods of time, and exposure to significant hazards. This study attempts to solve the problem of dynamic quantitative risk analysis of decision-making process for extreme group during complex cooperative scenarios. Based on an extended IDA team cognitive model, typical failure modes and performance quantification mechanisms of team communication were analyzed. The performance curve was utilized to distinctively model the cognitive behaviors of operators within the team, driving the cognitive transmission paths of group decision-making in a hybrid event tree. Error calibration of human error due to time uncertainty was conducted. The steps and techniques for dynamic risk analysis of decision-making of extreme groups under complex, dynamic, and ambiguous scenarios were summarized. The proposed method was validated through simulation in a typical powered diving task of manned submarine.

Reliability and availability simulation for vessel cooling system of the high temperature engineering test reactor

The article deals with the problem of risk analysis for new and unique nuclear facilities where safety-related systems are continuously operated during normal exploitation. A non-standard approach to system modeling was applied which goes beyond traditional method suitable for on-demand safety systems with a fixed mission time. As an example, the Vessel Cooling System (VCS) of the Japanese High Temperature Engineering Test Reactor (HTTR) has been analyzed in terms of life-cycle reliability and availability. Normal operation and emergency conditions have been considered, differing in the requirements for the system performance. In the former, the VCS reliability for cooling the biological shield around the Reactor Pressure Vessel (RPV) has been examined. In the latter, the VCS availability to remove the residual heat from the reactor core and RPV has been simulated. The results are of high importance for the safety and profitability of the HTTR-based electricity-hydrogen cogeneration plant (HTTR-GT/H2). The high reliability of the VCS under emergency conditions has been confirmed, thus contributing to the probabilistic safety assessment (PSA). Moreover, the fraction of the Forced Outage Rate (FOR) caused by VCS failures has been determined, thus contributing to the plant profitability studies.

A hybrid HFACS model using DEMATEL-ORESTE method with linguistic Z-number for risk analysis of human error factors in the healthcare system

The human error factor is one of the leading causes of medical errors. Among risk analysis techniques for human error factors, HFACS (Human Factor Analysis and Classification System) method has been regarded as one of the most valuable approaches due to its advantage in potential failure classification. However, the conventional HFACS method is insufficient to handle the risk analysis problem for human error factors considering the interactive relationship among these factors. Moreover, the current fuzzy HFACS frameworks cannot address quantitative risk analysis issues, including imprecision and reliability of information. Thus, this paper constructs an integrated linguistic Z-number-based HFACS framework for analyzing the risk of human error factors. This framework can capture the uncertainty and reliability of the risk evaluation information and the interactive relationships among factors. First, the linguistic Z-number-DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used to generate the comprehensive risk matrix of human error factors identified by the HFACS method. Then, an extended linguistic Z-number-ORESTE method based on the score function is presented to prioritize the risk of human error factors, which can show the preference, indifference, and incomparability relationship among human error factors. Finally, a case study of a healthcare system is conducted to illustrate the reliability of the proposed method. The result of this case indicates that inadequate resources are the most severe risk. Sensitivity analysis and comparative analysis indicate the necessity of considering the effect of the semantics of language terms and the interrelationships between human error factors on risk analysis results. These results show that the proposed hybrid framework is a reliable means to analyze the risk of human error factors within the HFACS method.

A New Right-Skewed One-Parameter Distribution with Mathematical Characterizations, Distributional Validation, and Actuarial Risk Analysis, with Applications

Skewed probability distributions are important when modeling skewed data sets because they provide a way to describe the shape of the distribution and estimate the likelihood of extreme events. Asymmetric probability distributions have the potential to handle and assess problems in actuarial risk assessment and analysis. To that end, we present a new right-skewed one-parameter distribution. In this work and for this purpose, a right-skewed probability distribution was derived and analyzed. The new distribution outperformed the exponential distribution, the Pareto distribution, the Chen distribution, and others in the field of actuarial risk analysis. Some useful key risk indicators are considered and analyzed to analyze the risks and for comparison with the competitive model. Several actuarial risk functions and indicators are evaluated and analyzed using the U.K. insurance claims data set. The process of risk assessment and analysis was carried out using a comprehensive simulation. For the purposes of distributional validity, a modified chi-squared type test is presented and employed in the testing process. The new, modified chi-squared type test that is used is simply an extension of the Rao–Robson–Nikulin test. In this work, the distributional validity is presented and analyzed under right-skewed censored and uncensored data sets.

Sensitivity Analysis of Risk Characteristics of Complex Engineering Systems: An Application to a Subsea Pipeline Monitoring System

One of the problems of risk analysis of complex engineering systems is the uncertainty of initial information about the time and damage associated with occurrence and development of the risk situation. The paper proposes a methodology and procedure for constructing a risk tree, loading it with initial data, calculating the corresponding characteristics: the distributions of time to reach the intermediate and main risk events and of associated with them damages, as well as their moments. Methodology involves the construction of the most dangerous path of risk situation development with respect to different criteria as well as analysis the sensitivity of risk characteristics to the initial information. The proposed approach is applied to a model of an automated system for remote monitoring of underwater gas pipeline. The proposed methodology and its implementation on a real-world example constitute the novelty of the work.

Seismic fragility analysis using stochastic polynomial chaos expansions

Within the performance-based earthquake engineering (PBEE) framework, the fragility model plays a pivotal role. Such a model represents the probability that the engineering demand parameter (EDP) exceeds a certain safety threshold given a set of selected intensity measures (IMs) that characterize the earthquake load. The-state-of-the art methods for fragility computation rely on full non-linear time–history analyses. Within this perimeter, there are two main approaches: the first relies on the selection and scaling of recorded ground motions; the second, based on random vibration theory, characterizes the seismic input with a parametric stochastic ground motion model (SGMM). The latter case has the great advantage that the problem of seismic risk analysis is framed as a forward uncertainty quantification problem. However, running classical full-scale Monte Carlo simulations is intractable because of the prohibitive computational cost of typical finite element models. Therefore, it is of great interest to define fragility models that link an EDP of interest with the SGMM parameters — which are regarded as IMs in this context. The computation of such fragility models is a challenge on its own and, despite a few recent studies, there is still an important research gap in this domain. This comes with no surprise as classical surrogate modeling techniques cannot be applied due to the stochastic nature of SGMM. This study tackles this computational challenge by using stochastic polynomial chaos expansions to represent the statistical dependence of EDP on IMs. More precisely, this surrogate model estimates the full conditional probability distribution of EDP conditioned on IMs. We compare the proposed approach with some state-of-the-art methods in two case studies. The numerical results show that the new method prevails over its competitors in estimating both the conditional distribution and the fragility functions.

The integrated prospect theory with consensus model for risk analysis of human error factors in the clinical use of medical devices

The risk analysis of human error factors is one of the most significant procedures for preventing and reducing risk in the clinical use of medical devices. Human Factor Analysis and Classification System (HFACS) framework, a systematic human error analysis tool, is widely used for human error factors risk analysis. However, the traditional HFACS framework is insufficient to deal with the scenario with complex and uncertain risk information and conflicting opinions among experts caused by their heterogeneous risk preferences and diverse knowledge. To address these limitations, this paper integrated the prospect theory with the consensus model under Interval Type-2 Fuzzy Sets (IT2FSs) environment for addressing the HFACS-based human error factors risk analysis problem. This integrated method enabled the HFACS to yield highly acceptable risk analysis results considering experts’ heterogeneous risk preferences. Specifically, the IT2FSs are utilized to represent highly complex and uncertain risk assessment information of human error factors. Secondly, the prospect theory is applied to model the heterogeneous risk preferences of experts and eliminate their impact on risk evaluation results. After obtaining the risk evaluation matrix by prospect theory, the consensual risk evaluation matrix is yielded by a consensus model that can balance the group aim of reaching a consensus and the individual aim of keeping original risk evaluation information as much as possible. Then, the risk ranking of human error factors is determined based on the distance of IT2FSs. Finally, a case study of the clinical use of ventilators, including sensitivity analysis and comparative analysis, is presented to illustrate the efficiency of the proposed method.

A complex spherical fuzzy CRADIS method based Fine-Kinney framework for occupational risk evaluation in natural gas pipeline construction

Occupational risk evaluation is one of the most indispensable issues in the risk prevention and control process for the natural gas pipeline construction (NGPC) project. The Fine-Kinney model, recognized as an effective occupational risk evaluation technique, has limited capability to handle the occupational risk analysis problem under the complex spherical fuzzy (CSF) environment. Accordingly, a synthetical Fine-Kinney framework based on the compromise ranking of alternatives from distance to ideal solution (CRADIS) method is developed to overcome these downsides of occupation risk analysis in the NGPC project within the CSF context. A prioritized weighted average (PWA) operator for complex spherical fuzzy numbers (CSFNs) is incorporated into the group risk evaluation matrix generation process, which can take the priority degrees of experts into account. Then, the extended CRADIS method-based Fine-Kinney framework is generated, in which the Choquet integral for CSFNs is incorporated to reflect the impact of interactive risk factors. Next, the detailed solution procedures of the framework for handling the occupational risk evaluation problem are presented. Finally, the described framework is employed as an empirical example of occupational risk analysis for the NGPC project to demonstrate its feasibility in practice. After that, a sensitivity analysis of the parameter is investigated to testify to the stability and rationality of the reported synthetical Fine-Kinney framework. Subsequently, to further display the advantages of the developed Fine-Kinney framework, a comparative study is implemented to discuss the evaluation result of occupational risk derived from the proposed framework and those of the existing similar Fine-Kinney frameworks. The analysis results indicate that the occupational risk (attack by human or animal) with the maximum risk priority value (1.000) using the framework is identified as the most serious risk for the NGPC project.

A Fermatean fuzzy Fine–Kinney for occupational risk evaluation using extensible MARCOS with prospect theory

The extant Fine–Kinney frameworks are insufficient to tackle the risk evaluation problem with Fermatean fuzzy information, in which the prioritization degrees and psychological characteristics of decision-makers are considered. Hence, this study develops a hybrid Fine–Kinney-based occupational risk evaluation framework with an extended Fermatean fuzzy MARCOS method (measurement of alternatives and ranking to Compromise solution). Such a MARCOS method improves conventional MARCOS by integrating Fermatean fuzzy prioritized weighted average operator and prospect theory. This improved method has the capability to handle the occupational risk analysis problem with Fermatean fuzzy data in the risk ranking procedure considering the prioritization degrees and bounded rational behavior of decision-makers. In addition, the Fermatean fuzzy numbers-based risk rating scales are established to transform the linguistic risk scores from decision-makers, it allows for handling uncertain risk rating information from decision-makers more effectively. Further, the improved MARCOS method is incorporated into the occupational risk ranking procedure, as it considers the decision-maker’s prioritization relationships among decision-makers and their reference point effect in occupational risk priority calculation. After that, an occupational risk analysis case for construction operations is selected to test the applicability and validity of the proposed framework. The result indicates that the occupational risk OR6 (Back injury) is the most serious risk with the lowest utility function value (-0.324), and OR7 (Tendinitis) is the least severe risk with the highest utility function value (0.682). Finally, sensitivity exploration and comparative study are implemented to further test the advantages of the developed framework.

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.

Health, safety, and environmental failure evaluation by hybridizing fuzzy multi-attribute decision-making methods for maritime scrubber systems

Many shipping companies have started using scrubbers in their fleet to eliminate sulfur emissions from ships, per IMO (International Maritime Organization) rules. Before and during the scrubbers' selection, the scrubbers' operational failures have also started to appear and cause serious concerns. In this study, classified scrubber types are explained and open type, closed type, and hybrid scrubber systems are evaluated. To contribute to this gap in the literature, scrubber failures were identified, five experts with different perspectives were consulted, and the most common and critical malfunctions were evaluated with the fuzzy best-worst method (F-BWM) and fuzzy technique for order preference by similarity to an ideal solution (F-TOPSIS). F-BWM was used to determine the importance weights of the risk parameters used in evaluating failures since it provides fewer comparisons among pairwise comparison-based decision-making methods and a more consistent judgment in the evaluation. F-TOPSIS, on the other hand, was used to determine the final priority scores of the scrubber failures, taking into account the risk parameter weights obtained in the first stage. It has been preferred due to its easy to useness and based on its closeness to the ideal solution and applicability to risk and failure analysis problems. Considering all different systems in general, important issues such as collection efficiency, sulfur emission problem, abrasion, leakages, pump failures, heat exchanger failures, air fan sealing failures, sensors and failures in monitoring the whole system have been investigated. Results show that too high axial velocity for separator and flooded separator, too high solids concentration in recirculation liquid (SF2), piping leakages (SF5), poor quality or inappropriate consumables and chemicals (SF11), and feed circulation pump problems (SF6) are found to be the most important problems among thirteen failures.

Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems

A challenging problem in risk and reliability analysis of Complex Engineering Systems (CES) is performing and updating risk and reliability assessments on the whole system with sufficiently high frequency. The challenge stems from both operational data complexity and systems’ complexity. The data complexity calls for novel and advanced data-driven methods, e.g., Deep Learning (DL). However, the systems’ complexity cannot be addressed only using black-box models; engineering knowledge and systems modeling methods must also be considered. In this paper, a novel mathematical architecture for operation condition and risk monitoring of CES is presented. In this architecture, a Bayesian Network (BN) is used to model the system, subsystems’ relations, scenarios leading to adverse events, and to fuse subsystem-level information. Further, Bayesian DL models are trained for subsystems’ diagnostics based on condition monitoring data, and their outputs are integrated into the root nodes of the designed BN. This integration enables addressing both the data and systems complexity in a single architecture that provides system-level insight. The proposed architecture also has the capacity to incorporate human inputs and qualitative information. We demonstrate the effectiveness of our proposed approach by performing a case study on a real-world Vapor Recovery Unit at an offshore oil production platform.

Achieving economic sustainability: operations research for risk analysis and optimization problems in the blockchain era

Achieving economic sustainability: operations research for risk analysis and optimization problems in the blockchain era

Spatial logical relationship analysis model of ship Encounter space

To improve the ability to analyze the spatial relationship between target ships, this study proposes a method for dividing the ship encounter space and analyzing spatial logical relationships using geographic information system (GIS) spatial analysis technology. The method solves the problem of ship collision risk analysis by studying the spatial distribution, spatial structure, and encounter characteristics of ships. In this model, it is necessary to divide ship encounter spaces to form a new spatial data model. The model then analyzes the spatial logical relationship between the optimal neighboring triangulation network and convex polygons in the ship encounter space and lastly obtains the trend information of the encounter danger and features of the encounter in the ship's navigation. This study conducted a model simulation using data of the M/T Sanchi and M/V CF Crystal collision on January 6, 2018, and analyzed the collision risk characteristics during four periods prior to the collision. The results show that the model is effective and feasible for accurately extracting ship navigation safety information.

A Z-number integrated weighted MULTIMOORA method for risk prioritization in FMEA

Current risk prioritization approaches for FMEA models are insufficient to cope with risk analysis problem in which the self-confidence of expert’s judgment and the deviation among risk evaluation information are considered, simultaneously. Therefore, to remedy this limitation, this paper reports an extended risk prioritization approach by integrating the MULTIMOORA approach, Z-numbers and power weighted average (PWA) operator. Firstly, the Z-numbers with triangular fuzzy numbers are applied to reflect the self-confidence and uncertainty of expert’s judgment. Next, the PWA operator for Z-numbers (Z-PWA) with similarity measure is proposed to obtain the group risk evaluation matrix by considering the influence of the deviation among risk evaluation information. Then, an extended version of MULTIMOORA method with developed entropy method is presented to calculate risk priority ranking order of each failure. Finally, the equipment failures in a certain oil and gas plant is utilized to test the extended risk prioritization approach for FMEA model. After that, the sensitivity and comparison studies are led to illustrate the availability and reliability of the proposed risk prioritization approach for FMEA based risk analysis problem.

A likelihood‐based ORESTE method for failure mode and effect analysis (FMEA) based risk analysis problem under interval type‐2 fuzzy environment

AbstractThe failure mode and effect analysis (FMEA) model is an effective safety system analysis method to identify and analyze potential risks. Nevertheless, the traditional FMEA possesses deficiencies associating with questionable multiplication procedure, overlooking uncertainty and interaction relationships among risk factors. Besides, determining the risk priority considering the detailed interrelation among the failure modes is beyond the capability of current FMEA models, especially in the case of complex preference information. Thus, this paper proposed an extended FMEA by integrating ORESTE (organization, rangement et Synthèse de données relarionnelles, in French) method, Heronian Mean (HM) operator, and interval type‐2 fuzzy sets (IT2FSs). First, the IT2FSs are utilized to depict the complex risk preference information. Second, the HM operator, as an aggregated tool, is utilized to obtain collective evaluation information, which can fully reflect the overall correlations among individual risk assessment information. Then, an extended ORESTE method based on the likelihood of IT2FSs is utilized to obtain the priority ranking orders of failure modes. In this extended method, a new IT2FSs global preference score is constructed to determine the weak ranking. And, three new preference intensities based on the likelihood of IT2FSs are constructed to determine PIR (preference, indifference, and incomparability) structure which can show the detailed relationships between failure modes. Finally, an illustrative example of a nuclear reheat valve system is selected to verify its application and efficiency.

Occupational health and safety risk assessment using an integrated TODIM‐PROMETHEE model under linguistic spherical fuzzy environment

Occupational health and safety (OHS) is a multidisciplinary activity aimed at recognizing, evaluating and controlling hazards arising in or from the workplace that could impair the health and well-being of workers. In dealing with an OHS risk analysis problem, it is a crucial step and also a big challenge to assess the risk of occupational hazards. Normally, domain experts tend to use qualitative words to express the risk of occupational hazards due to the fuzziness of human cognition, and multiple risk criteria are often involved in the OHS risk analysis. In this study, we develop a new OHS risk assessment framework by integrating the TODIM (an acronym in Portuguese for interactive and multicriteria decision-making) and preference ranking organization method for enrichment evaluations (PROMETHEE) methods to assess and rank the risk of occupational hazards under linguistic spherical fuzzy environment. The linguistic spherical fuzzy sets are utilized to deal with the vague and uncertain risk assessments of occupational hazards provided by experts. An integrated TODIM-PROMETHEE algorithm is introduced to determine the risk priority of the identified occupational hazards. Moreover, we extend the indifference threshold-based attribute ratio analysis method to derive the relative weights of risk criteria based on linguistic spherical fuzzy information. Finally, the OHS risk analysis of medical staff in a hospital is presented to illustrate the effectiveness and availability of our proposed model. Results show that the TODIM-PROMETHEE framework being developed in this study provides a useful, effective, and practical way for the risk assessment of occupational hazards in OHS.

Relational Theory of Risk and Its Applications to Game Theory Problems of Non-Numerical Economics

The purpose of the work is to study the foundations of a general risk theory. To form a single formal concept of risk, a number of definitions of the term “risk” found in the literature have been analyzed. There is a certain redundancy in the number of entities involved in the definition of this term. The necessary attributes of the genesis of this concept have been identified. Based on the analysis, using the instrumentation of modern algebra, a new formal, mathematically strict definition of risk is built. In fact, the paper proposes a new relational theory of risk, attracting only two entities to define the concept of “risk”: set and order of preference, inducing on this set the minimum structure of a semilattice or family of semilattices. The paper also describes an approach for studying in theoretical-risk setting problems in which there is no risk, in the traditional sense, in which the preference relation does not induce a semilattice and/or is a preorder. It is shown that in this case, when identifying a suitable equivalence relation on a set of outcomes, the problem can be reduced to a classical theoretical-risk setting. The second part of the paper contains an example of the direct use of a new relational risk theory in the study of nonnumerical economies. The problem of analysis of the situation of confrontation between two technologically unequal countries in game-theoretical staging is considered. We are talking about a grandiose space program - the organization of manned flights to Mars. The scales of the object of confrontation, the impossibility of quantitative assessments of the consequences of the implementation of scenarios of this project, make any quantitative assessments impossible at the stage of preliminary analysis. Therefore, only expert estimates of preferences at multiple outcomes can be used as initial data for confrontation analysis. Under these conditions, the emergence of certain risks of the project implementation for both players was demonstrated. During the analysis of the example illustrating the application of the new relational risk theory, a number of optimality principles considered in game theory were extended to the case when only partial orders are given on a set of game outcomes. As the methodological basis of the research we used the achievements of modern algebra, in particular the theory of relational systems, as well as the concepts and methods of game theory, such as representing the game in normal form, selecting dominant and eliminating dominant strategies, choosing solutions from many cautious strategies, as well as from the set of Nash Equilibria. The main result of the paper is the substantiation of relational risk theory, the formation of its conceptual base, the demonstration of the constructive nature of the theory on the example of solving a specific problem of risk analysis in an economic system described in terms of non-numerical characteristics. The material presented in the article is of interest to researchers in the field of risk theory and game theory, as well as to practitioners engaged in socio-economic and political forecasting in conditions of lack of information.

Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Unmanned aerial systems (UASs) are continuing to proliferate. Quantitative risk assessment for UAS operations, both a priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. In this paper, the problem of UAS risk analysis and decision making is treated through a novel application of Dempster–Shafer (DS) networks using auto-updating transition matrices. This method was motivated by the results of the 2018 UAS Safety Symposium held at the Georgia Institute of Technology, which was conducted as part of the research detailed in this paper. The paper describes training a DS network based on simulated operation data, testing the capabilities of the trained network to make real-time decisions on a small UAS against a baseline system in a representative mission, and exploring how this system would extend to a more inclusive UAS ecosystem. Conclusions are drawn with respect to the research performed, and additional research directions are proposed.