scholarly journals ASSESSMENT AND MANAGEMENT OF SAFETY RISKS THROUGH HIERARCHICAL ANALYSIS IN FUZZY SETS TYPE 1 AND TYPE 2: A CASE STUDY (FARYAB CHROMITE UNDERGROUND MINES)

2021 ◽  
Vol 36 (3) ◽  
pp. 1-17
Author(s):  
Mohammad Reza Ameri Siahuei ◽  
Mohammad Ataei ◽  
Ramin Rafiee ◽  
Farhang Sereshki
Keyword(s):  
Risk Assessment ◽  
Fuzzy Sets ◽  
Underground Mine ◽  
Underground Mines ◽  
Mine Safety ◽  
Safety Risks ◽  
Proper Scaling ◽  
Blast Scaling

There is a high rate of casualty among miners in the world every year. One way to reduce accidents and increase safety in mines is to use the risk management process to identify and respond to major hazards in mines. The present study is an attempt to investigate the assessment and management of safety risks in Faryab chromite underground mines. In this paper, the method of AHP in type-1 and type-2 fuzzy sets is used for risk assessment. Upon studying two underground mines of Faryab chromite (Makran and Nemat), 45 hazards were divided into 9 groups, among which 7 main risks were eventually identified. The risk assessment showed that the most important hazards in the Nemat underground mine are the required airflow, the lack of proper scaling and post-blast scaling. Similarly, the assessment of hazards in the Makran underground mine showed that post-blast scaling, absence of proper scaling, and proper ventilation of dust, are the most important hazards. Finally, after detecting the causes of the accidents, based on the records of accidents at the mine safety, health, and environmental unit, technical personnel’s descriptions, and similar risk projects, proper responses are prepared for each group of hazards.

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

Standard complication screening information can be used for risk assessment for first time foot ulcer among patients with type 1 and type 2 diabetes

2019 ◽  
Vol 151 ◽  
pp. 177-186 ◽  
Author(s):  
Sine Hangaard ◽  
Anne Rasmussen ◽  
Thomas Almdal ◽  
Annemette Anker Nielsen ◽  
Kirsten Engelhart Nielsen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Foot Ulcer ◽  
First Time

Risk assessment for the industrial network based on interval type-2 fuzzy sets

Kybernetes ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 916-937
Author(s):  
Chao Ren ◽  
Xiaoxing Liu ◽  
Zongqing Zhang
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Risk Evaluation ◽  
Quantitative Risk Assessment ◽  
Content Type ◽  
Industrial Network ◽  
Risk Magnitude ◽  
Interval Type

Purpose The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment. Design/methodology/approach This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters. Findings The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method. Research limitations/implications There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study. Originality/value The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

An interval type-2 fuzzy ANP approach to evaluate enterprise technological innovation ability

Kybernetes ◽  
2016 ◽  
Vol 45 (9) ◽  
pp. 1486-1500 ◽  
Author(s):  
Tong Wu ◽  
Xinwang Liu
Keyword(s):  
Fuzzy Sets ◽  
Evaluation Model ◽  
Influence Factors ◽  
Content Type ◽  
Fuzzy Anp ◽  
Enterprise Technology ◽  
Innovation Ability ◽  
Interval Type

Purpose The purpose of this paper is to overcome the drawbacks of analytic hierarchy process in solving complex decision-making problems, especially for the evaluation of enterprise technology innovation ability (ETIA). Because interval type-2 fuzzy sets (IT2 FSs) can handle uncertainty linguistic variables in a more flexible and precise way than type-1 fuzzy sets with their second fuzzy membership functions, a fuzzy ANP method with IT2 FSs is proposed to evaluate the ETIA. Design/methodology/approach The criteria of evaluation on ETIA are identified and an evaluation model for ETIA is constructed on the basis of the application analysis of ETIA and theoretical design of ANP. In addition, two different ranking methods of IT2 FSs are applied in processing the relationships between influence factors of ETIA. Findings By using the proposed interval type-2 fuzzy ANP (IT2 FANP) method, the efficiencies of the whole evaluation of ETIA can be measured and the important factors in the ETIA can also be determined. Compared with the type-1 FANP through the ranking results, the proposed IT2 FANP is more reasonable and robust for the evaluation of ETIA. Practical implications The proposed IT2 FANP method is applied on the evaluation of ETIA. With respect to the application, the proposed method can be used to evaluate many more complex problems that contain feedback and circular relationships. Originality/value The proposed IT2 FANP approach can solve the complexities and uncertainties at the same time. Considering the subjective initiative of decision-makers and the feedback between influence factors, the proposed method is more efficient than the existing type-1 approaches in the literature.

scholarly journals Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zuzana Janková ◽  
Petr Dostál
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Stock Market ◽  
Fuzzy Sets ◽  
Financial Time Series ◽  
Fuzzy Model ◽  
Exchange Traded Funds ◽  
International Equity Markets

Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

Multi-criteria decision-making method based on type-2 fuzzy sets

Filomat ◽  
2017 ◽  
Vol 31 (2) ◽  
pp. 431-450 ◽  
Author(s):  
Jing Wang ◽  
Qing-Hui Chen ◽  
Hong-Yu Zhang ◽  
Xiao-Hong Chen ◽  
Jian-Qiang Wang
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Computational Models ◽  
Fuzzy Numbers ◽  
Multi Criteria Decision Making ◽  
Interval Numbers ◽  
Interval Type ◽  
Uncertainty Information

Type-2 fuzzy sets (T2FSs) are the extension of type-1 fuzzy sets (T1FSs), which can convey more uncertainty information in solving multi-criteria decision-making (MCDM) problems. Motivated by the extension from interval numbers to triangular fuzzy numbers, three-trapezoidal-fuzzy-number-bounded type-2 fuzzy numbers (TT2FNs) are defined on the basis of interval type-2 trapezoidal fuzzy numbers (IT2TFNs), and they can convey more uncertainty information than T1FSs and IT2FSs. Moreover, the drawbacks of the existing computational models of generalized fuzzy numbers are analyzed, and a new computational model of fuzzy numbers is proposed, which is further extended to TT2FNs. Besides, a MCDM method is proposed to deal with the evaluation information given in the form of TT2FNs. Finally, an illustrative example and comparison analysis are provided to demonstrate the feasibility and validity of the proposed method.

Estimating Levels of Learning Outcomes Acquirement Based on Fuzzy Sets, Relations, and Their Compositions

2021 ◽  
pp. 85-108
Author(s):  
Aleksandra Mreła ◽  
Oleksandr Sokolov
Keyword(s):  
Fuzzy Sets ◽  
Learning Outcomes ◽  
Learning Outcome ◽  
Test Results ◽  
Linguistic Terms ◽  
Knowledge And Skills ◽  
Fuzzy Input ◽  
Better Than

New curriculum designers should prepare the set of learning outcomes that will be studied by students, and their acquirement will be verified by teachers. It is not easy to estimate whether students achieved the required learning outcomes even in the range of knowledge and skills, but when the competencies are considered, it becomes harder. Because of the convenience of using the linguistic terms (excellent, poor, good, and so on), it is better to apply fuzzy relations (type 1 if the numbers are chosen or type 2 if the sets are used), which can handle with the linguistic terms better than classical relations. The estimation of the learning outcome's acquirement can be conducted on the bases of a test or tests. For calculating the levels of learning outcome's acquirement, two fuzzy input relations are designed. Experts build one based on their knowledge, and the second one is based on the test results. The output relation, showing levels of learning outcome/outcomes' acquirement, is built with the application of the S-T composition of the first and second relations.

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