Dutch Journal of Finance and Management

Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase
Estefania del Pilar Leal 1, Alejandro Peña 1 * , Lina Sepúlveda-Cano 1, João Vidal Carvalho 2
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1 School of Management, EAFIT University, Medellin, Colombia
2 CEOS, Porto Accounting and Business School, Polytecnhic University of Porto, Portugal
* Corresponding Author
Research Article

Dutch Journal of Finance and Management, 2023 - Volume 6 Issue 2, Article No: 25209
https://doi.org/10.55267/djfm/14129

Published Online: 30 Dec 2023

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How to cite this article
APA 6th edition
In-text citation: (Leal et al., 2023)
Reference: Leal, E. D. P., Peña, A., Sepúlveda-Cano, L., & Carvalho, J. V. (2023). Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase. Dutch Journal of Finance and Management, 6(2), 25209. https://doi.org/10.55267/djfm/14129
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Leal EDP, Peña A, Sepúlveda-Cano L, Carvalho JV. Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase. DUTCH J FINANCE MANA. 2023;6(2):25209. https://doi.org/10.55267/djfm/14129
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Leal EDP, Peña A, Sepúlveda-Cano L, Carvalho JV. Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase. DUTCH J FINANCE MANA. 2023;6(2), 25209. https://doi.org/10.55267/djfm/14129
Chicago
In-text citation: (Leal et al., 2023)
Reference: Leal, Estefania del Pilar, Alejandro Peña, Lina Sepúlveda-Cano, and João Vidal Carvalho. "Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase". Dutch Journal of Finance and Management 2023 6 no. 2 (2023): 25209. https://doi.org/10.55267/djfm/14129
Harvard
In-text citation: (Leal et al., 2023)
Reference: Leal, E. D. P., Peña, A., Sepúlveda-Cano, L., and Carvalho, J. V. (2023). Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase. Dutch Journal of Finance and Management, 6(2), 25209. https://doi.org/10.55267/djfm/14129
MLA
In-text citation: (Leal et al., 2023)
Reference: Leal, Estefania del Pilar et al. "Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase". Dutch Journal of Finance and Management, vol. 6, no. 2, 2023, 25209. https://doi.org/10.55267/djfm/14129
ABSTRACT
One of the significant challenges facing the aviation sector is the management of risks arising from its flight operations, especially in the approach and landing phases, where pilot experience and training are of great importance and where the most significant incidents for air safety occur. Therefore, this paper proposes a model inspired by the structure of a Fuzzy ELECTRE model for managing the operational risks that arise in the approach and landing phases that can lead to safety events. Thanks to the analysis of the literature collected, the management criteria and risk parameters to be taken into account for these two flight phases were shown following air safety manuals such as the International Civil Aviation Organization (ICAO) manual, and where the data obtained was obtained qualitatively thanks to the implementation of surveys with expert pilots, whose information served as the primary input for the characterisation of risks. Following the structure of the proposed model, five (5) reference risk scenarios management were constructed using the previous information, and an analysis of the dominance and discrepancy of a risk scenario vs. the previously established reference scenarios was carried out. Finally, it can be concluded that the proposed model allowed the quantitative-qualitative characterisation for managing the most relevant risks in the approach and landing phases, integrating the expertise of experts in this area.
KEYWORDS
REFERENCES
  • Basel Committee on Banking Supervision (2006). International Convergence of Capital Measurement and Capital Standards. https://www.bis.org/publ/bcbs128_es.pdf
  • Benoit, F., Monstein, R., Waltert, M., & Morio, J. (2023). Data-driven mid-air collision risk modelling using extreme-value theory. Aerospace Science and Technology. 142 (Part A), 1-2. DOI: https://doi.org/10.1016/j.ast.2023.108646
  • Bills, K., Costello, L., & Cattani, M. (2023). Major aviation accident investigation methodologies used by ITSA members. Safety Science, 168(2023), 1-2. DOI: https://doi.org/10.1016/j.ssci.2023.106315
  • Boeing (2023). Statistical Summary of Commercial Jet Airplane Accidents Worldwide Operations | 1959‑2022.
  • Cadena, S., & Garcia, D. (2023). Aeronautical operational risk characterisation survey on approach and landing phase [Unpublished raw data]. Eafit University.
  • Caetano, M (2023). Aviation accident and incident forecasting combining occurrence investigation and meteorological data using machine learning. Aeronautics Institute of Technology. 27(1), 48-55. DOI: https://doi.org/10.3846/aviation.2023.18641
  • Chan, W., T.-K., & Li, W. (2023). Development of effective human factors interventions for aviation safety management. Frontiers in public health, 11. DOI: https://doi.org/10.3389/fpubh.2023.1144921
  • Comas, R., Campaña, L., Beltrán J. (2020). Evaluation of the company's internal control by applying neutrosophic AHP. operational research journal, 41(5), 683. https://rev-invope.pantheonsorbonne.fr/sites/default/files/inline-files/41520-10.pdf
  • Del Rio, J. (2023). Aeronautical operational risk characterisation survey on approach and landing phase [Unpublished raw data]. Eafit University. DOI: 10.1016/j.treng.2021.100087
  • Dong, T., Yang, Q., Ebadi, N., Luo, R., & Rad, P. (2021). Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach. Journal of Advanced Transportation. 2021, 1-4. DOI: https://doi.org/10.1155/2021/5540046
  • Erazo, J. (2023). Aeronautical operational risk characterisation survey on approach and landing phase [Unpublished raw data]. Eafit University.
  • Faberio, J. A. (2022). Operational risk management: a relevant factor in MSEs. [Thesis, Universidad Católica Santo Toribio de Mogrovejo] DOI: https://tesis.usat.edu.pe/bitstream/20.500.12423/4974/1/TIB_FaberioZambranoAngelo.pdf
  • Federal Aviation Administration. (2012). Safety Management System Manual. [PDF] chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.faa.gov/air_traffic/publications/media/ATO-SMS-Manual.pdf
  • Federal Aviation Administration. (2020). Civil aviation model regulations. (Version 2.10).
  • Garces, C. Gonzalez, H. (2023). Aeronautical operational risk characterisation survey on approach and landing phase [Unpublished raw data]. Eafit University.
  • H. Taghipour, A., Parsa, A., Mohammadian, K. A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources, Transportation Engineering 2 (2020) 100025. DOI: https://doi.org/10.1016/j.treng.2020.100025.
  • International Air Transport Association. (2021). 2021 safety report. (58 edition).
  • International Civil Aviation Organisation. (2018). Operational Security Management Handbook (SMM). (Tercera edición). [PDF]. https://www.aerocivil.gov.co/autoridad-de-laaviacioncivil/bibliotecatecnica/Gestin%20de%20Seguridad/Documento%20OACI%209859%20-%20tercera%20edici%C3%B3n%202013.pdf
  • International Civil Aviation Organisation. (2023). Annual Safety Report. (Thirteen edition). [PDF]. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.icao.int/RASGPA/RASGPADocuments/ASR2023-13-SE.pdf
  • Loyaga, R., Malqui, E. (2019). Level of criticality of the operational risk of the process of transport and delivery of materials in the company central Dino SAC Trujillo Industrial Park [Thesis, Universidad Privada Antenor Orrego], Digital Repository of the Universidad Privada Antenor Orrego. DOI: https://hdl.handle.net/20.500.12759/5655
  • Memarzadeh, M., Matthews, B., & Avrekh, I. (2020). Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder. Aerospace, 7(8), 115. DOI: https://doi.org/10.3390/aerospace7080115
  • Odisho, E., & Truong, D. (2021). Applying machine learning to enhance runway safety through runway excursion risk mitigation. Paper presented at the Integrated Communications, Navigation and Surveillance Conference, ICNS, (2021-April). DOI: 10.1109/ICNS52807.2021.9441554
  • Peña, A., Bonet, I., Lochmuller, C., Tabares, M. S., Piedrahita, C. C., Sánchez, C. C., Giraldo Marín, L. M., Góngora, M., & Chiclana, F. (2019). A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs. Soft Computing, 23(20), DOI: 10537–10550. https://doi.org/10.1007/s00500-018-3625-8
  • Puranik, T. (2018) A methodology for quantitative data-driven safety assessment for general aviation [Doctoral thesis, Escuela Técnica Superior de Ingeniería Aeroespacial [Doctoral thesis, School of Aerospace Engineering]. Institutional repository School of Aerospace Engineering. [PDF]. file:///C:/Users/Usuario/OneDrive%20-%20Universidad%20EAFIT/Escritorio/PURANIK-DISSERTATION-2018.pdf
  • Rey, M., Aloise, D., Soumis, F., & Pieugueu, R. (2021). A data-driven model for safety risk identification from flight data analysis. Transportation Engineering, (5).
  • Ríos Insua, D., Alfaro, C., Gómez, J., Hernández-Coronado, P., Bernal, F. (2018). A framework for risk management decisions in aviation safety at state level. Reliability Engineering and System Safety. 179 (2018), 74–82. ISSN 0951-8320, DOI: https://doi.org/10.1016/j.ress.2016.12.002.
  • Rouyendegh, B., & Erol, S. (2012). The Intuitionistic Fuzzy ELECTRE model. International Journal of Management Science and Engineering Management. 13(2), 139-145. DOI: 10.1080/17509653.2017.1349625
  • Special Administrative Unit of Civil Aeronautics. (2011). Flight data analysis programme. [PDF]. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.aerocivil.gov.co/normatividad/NormatividadAeronautica/Circulares%20Seguridad%20Area/12-Programa%20FDA.pdf
  • Zhang, X., & Mahadevan, S. (2021). Bayesian network modelling of accident investigation reports for aviation safety assessment. Reliability Engineering and System Safety, 209 DOI: 10.1016/j.ress.2020.107371
  • Zhao, W., Li, L., Alam, S., & Wan, Y. (2021). An incremental clustering method for anomaly detection in flight data. Transportation Research Part C: Emerging Technologies. 132 (103406), 1-27. DOI: https://doi.org/10.1016/j.trc.2021.103406
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