Special Issue "Applications of Fuzzy Modeling in Risk Management"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Set Theory".

Deadline for manuscript submissions: 31 October 2022 | Viewed by 1958

Special Issue Editors

Dr. Edit Toth-Laufer
E-Mail Website
Guest Editor
Institute of Mechatronics and Vehicle Engineering, Donát Bánki Faculty of Mechanical and Safety Engineering, Óbuda University, 1081 Budapest, Hungary
Interests: fuzzy systems; fuzzy decision making; risk assessment; complexity reduction
Prof. Dr. László Pokorádi
E-Mail Website
Guest Editor
Institute of Mechatronics and Vehicle Engineering, Donát Bánki Faculty of Mechanical and Safety Engineering, Óbuda University, 1081 Budapest, Hungary
Interests: mathematical modeling of maintenance processes; application of risk management in aviation; application of fuzzy models in maintenance management

Special Issue Information

Dear Colleagues,

Risk management has long been an important area of engineering, environmental and health research, but has become even more of a focus today due to the emergence of the COVID-19 pandemic.   

Among the risk factors, quantitative and qualitative parameters can be observed as well, and these kinds of systems are full of uncertainty and subjectivity in the data and in evaluation process. The above characteristics justify the use of soft computation methods, especially fuzzy logic-based models.

In many cases, real-time risk management is required, where the short reaction time has vital importance. However, due to the large number of risk parameters and the complexity of their context result in the complexity of the model, which should be handled adequately. Consequently, for the application of different reduction techniques, anytime algorithms are essential. 

This Special Issue invites original contributions, new developments of classical results, and advanced topics of high potential for future research and applications in different field of risk management using fuzzy models.

Potential topics include, but are not limited to:

  • Risk management and risk assessment in any field;
  • Multicriteria decision making;
  • Predictive models;
  • Hybrid risk assessment or risk management models;
  • Real-time risk assessment and management;
  • Complexity reduction techniques in fuzzy models.

Dr. Edit Toth-Laufer
Prof. Dr. László Pokorádi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • risk management
  • risk assessment
  • fuzzy reasoning
  • multiple criteria decision
  • expert systems
  • fuzzy applications
  • hybrid systems
  • neuro-fuzzy systems
  • uncertain information

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments
Mathematics 2022, 10(10), 1699; https://doi.org/10.3390/math10101699 - 16 May 2022
Viewed by 216
Abstract
Rockburst is a severe geological disaster accompanied with the violent ejection of rock debris, which greatly threatens the safety of underground workers and equipment. This study aims to propose a novel multi-criteria decision-making (MCDM) approach for evaluating rockburst risk under uncertain environments. First, [...] Read more.
Rockburst is a severe geological disaster accompanied with the violent ejection of rock debris, which greatly threatens the safety of underground workers and equipment. This study aims to propose a novel multi-criteria decision-making (MCDM) approach for evaluating rockburst risk under uncertain environments. First, considering the heterogeneity of rock mass and complexity of geological environments, trapezoidal fuzzy numbers (TrFNs) are adopted to express initial indicator information. Thereafter, the superiority linguistic ratings of experts and a modified entropy weights model with TrFNs are used to calculate the subjective and objective weights, respectively. Then, comprehensive weights can be determined by integrating subjective and objective weights based on game theory. After that, the organísation, rangement et synthèse de données relarionnelles (ORESTE) approach is extended to obtain evaluation results in a trapezoidal fuzzy circumstance. Finally, the proposed approach is applied to assess rockburst risk in the Kaiyang phosphate mine. In addition, the evaluation results are compared with empirical methods and other trapezoidal fuzzy MCDM approaches. Results show that the proposed extended ORESTE approach is reliable for evaluating rockburst risk, and provides an effective reference for the design of prevention techniques. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
Show Figures

Figure 1

Article
Bipolar Dissimilarity and Similarity Correlations of Numbers
Mathematics 2022, 10(5), 797; https://doi.org/10.3390/math10050797 - 02 Mar 2022
Viewed by 448
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
Show Figures

Figure 1

Article
FMEA in Smartphones: A Fuzzy Approach
Mathematics 2022, 10(3), 513; https://doi.org/10.3390/math10030513 - 05 Feb 2022
Viewed by 392
Abstract
Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from [...] Read more.
Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results. Full article
(This article belongs to the Special Issue Applications of Fuzzy Modeling in Risk Management)
Show Figures

Figure 1

Back to TopTop