Fuzzy cognitive maps in systems risk analysis: a comprehensive review
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SURVEY AND STATE OF THE ART
Fuzzy cognitive maps in systems risk analysis: a comprehensive review Ezzeddin Bakhtavar1,2
· Mahsa Valipour3 · Samuel Yousefi3 · Rehan Sadiq2 · Kasun Hewage2
Received: 5 September 2020 / Accepted: 28 October 2020 © The Author(s) 2020
Abstract Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study. Keywords System risk · Error · Fault · Failure · Fuzzy cognitive map
Abbreviations BBN ERP FCMs FGCMs HFCMs NFCMs FIS FMEA GA MS-FCMs NHL PSO TOPSIS MOORA
B
Bayesian belief networks Enterprise resource planning Fuzzy cognitive maps Fuzzy grey cognitive maps Hesitant fuzzy cognitive maps Neuro-fuzzy cognitive map Fuzzy inference system Failure mode and effects analysis Genetic algorithm Multi-stage fuzzy cognitive maps Nonlinear Hebbian learning Particle swarm optimization Technique for order of preference by similarity to ideal solution Multi-objective optimization on the basis of ratio analysis
Ezzeddin Bakhtavar [email protected]; [email protected]
1
Faculty of Mining and Materials Engineering, Urmia University of Technology, Band Road, 5716693188 Urmia, Iran
2
School of Engineering, University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada
3
Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
PROMETHEE Preference ranking organization method of enrichment evaluation ISM Interpretative structural modeling DEA Data envelopment analysis SEM Structural equation model
Introduction The modern world relies on sophisticated human systems and new technologies to make decisions in the presence of uncertainty. As a result, decision-makers attempt to control the risks that arise from the complexity of those systems when making decisions [69]. Exposure to risks is inevitable in fields such as management, engineering, medicine, etc. Decisionmakers use techniques to assess potential risks within the scope of risk assessment and to analyze the causes and effects associated with them [101]. Therefore, a variety of qualitative and quantitative techniques have been developed and applied in various sciences and industries [155]. However, the complexity of a technique for analyzing the r
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