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Research Article

Controlling aeroplane crashing through aviation infrastructure using multi-agent technology

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Received 03 Apr 2023, Accepted 07 Feb 2024, Published online: 27 Mar 2024

References

  • Low JMW, Yang KK. An exploratory study on the effects of human, technical and operating factors on aviation safety. J Transp Saf Secur. 2019;11(6):595–628. doi: 10.1080/19439962.2018.1458051.
  • Safety Report 2022 Edition. 2022. Online]. Available from: www.icao.int
  • Accident involving M/S Air India Express B737-800 aircraft Vt on 07 August aircraft accident investigation bureau ministry of civil aviation Government of India, 2020.
  • Ud-Din S, Yoon Y. Analysis of loss of control parameters for aircraft maneuvering in general aviation. J Adv Transp. 2018;2018:1–19. doi: 10.1155/2018/7865362.
  • Kelly D, Efthymiou M. An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017. J Safety Res. 2019;69:155–165. doi: 10.1016/j.jsr.2019.03.009.
  • Bazargan M, Guzhva VS. Impact of gender, age and experience of pilots on general aviation accidents. Accid Anal Prev. 2011;43(3):962–970. doi: 10.1016/j.aap.2010.11.023.
  • 2021 Safety report flight safety foundation. [Online]Available from: https://aviation-safety.net/database/
  • Wilke S, Majumdar A, Ochieng WY. Airport surface operations: a holistic framework for operations modeling and risk management. Saf Sci. 2014;63:18–33. doi: 10.1016/j.ssci.2013.10.015.
  • Dorri A, Kanhere SS, Jurdak R. Multi-Agent systems: a survey. IEEE Access. 2018;6:28573–28593. doi: 10.1109/ACCESS.2018.2831228.
  • Sharma A, Sharma SK. Analyzing the role of multiagent technology in preventing airplane crash using AHP and DEMATEL approach. Int J Crashworthiness. 2022;27(6):1753–1769. doi: 10.1080/13588265.2021.2008739.
  • Ekman SK, Debacker M. Survivability of occupants in commercial passenger aircraft accidents. Saf Sci. 2018;104:91–98. doi: 10.1016/j.ssci.2017.12.039.
  • Zdobyslaw Goraj. (2004). AN OVERVIEW OF THE DEICING AND ANTIICING TECHNOLOGIES WITH PROSPECTS FOR THE FUTURE. Warsaw University of Technology, 24TH INTERNATIONAL CONGRESS OF THE AERONAUTICAL SCIENCES, 1–11.
  • Nadeeka C, Kapila RM, Rathnayaka T, et al. Multi-agent based intelligent weather forecasting system novel forecasting mechanism on time series view project remote sensing and GIS view project. Available from: https://www.researchgate.net/publication/335632052
  • M. R. Ioan and S. P. Liliana, “Using Mobile Agents and Intelligent Data Analysis Techniques for Climate Environment Modeling and Weather Analysis and Prediction,” 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, Timisoara, Romania, 2008, pp. 316–319, doi: 10.1109/SYNASC.2008.51.
  • Sandy Dance and Rodney Potts. (2002). Microburst Detection Using Agent Networks. Journal of Atmospheric and Oceanic Technology, 19, 646–653.
  • Koglbauer I. Simulator training improves the estimation of collision parameters and the performance of student pilots. Procedia Soc Behav Sci. 2015;209:261–267. doi: 10.1016/j.sbspro.2015.11.231.
  • Tegos S, Demetriadis S, Tsiatsos T. A configurable conversational agent to trigger students’ productive dialogue: a pilot study in the CALL domain. Int J Artif Intell Educ. 2014;24(1):62–91. doi: 10.1007/s40593-013-0007-3.
  • Gorodetsky V, Karsaev O, Samoylov V, et al. Multi-agent technology for air traffic control and incident management in airport airspace Performance Simulation Initiative (PSI) view project personal assistance recommendation system view project multi-agent technology for air traffic control and incident management in airport airspace. Available from: https://www.researchgate.net/publication/228922255
  • Nakamura S, Furuta K, Kanno T, et al. Multi -Agent simulation of ground aircraft operations at a large airport. SIMUTools 2010 - 3rd International ICST Conference on Simulation Tools and Techniques; ICST; 2010, pp. 1-6. doi: 10.4108/ICST.SIMUTOOLS2010.8724.
  • Chen F. Aircraft taxiing route planning based on multi-agent system. Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016; Piscataway (NJ): Institute of Electrical and Electronics Engineers Inc.; 2017. p. 1421–1425. doi: 10.1109/IMCEC.2016.7867448.
  • Stroeve SH, Blom HAP, Bakker GJ. Contrasting safety assessments of a runway incursion scenario: event sequence analysis versus multi-agent dynamic risk modelling. Reliab Eng Syst Saf. 2013;109:133–149. doi: 10.1016/j.ress.2012.07.002.
  • Muller A, Crespo Marquez A, Iung B. On the concept of e-maintenance: review and current research. Reliab Eng Syst Saf. 2008;93(8):1165–1187. doi: 10.1016/j.ress.2007.08.006.
  • Sharma SK, Vishwakarma S, Jha N. Prognosis agent technology: influence on manufacturing organizations. Int J Adv Manuf Technol. 2017;92(1–4):435–446. doi: 10.1007/s00170-017-0025-7.
  • Daiping H, Weiguo X, Huiming D, et al. An agent based fault diagnosis support system and its application. Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE); 2007. p. 388–392. doi: 10.1109/soli.2006.329003.
  • Li X, Zhang C, Gao L, et al. An agent-based approach for integrated process planning and scheduling. Expert Syst Appl. 2010;37(2):1256–1264. doi: 10.1016/j.eswa.2009.06.014.
  • Alonso-Portillo I, Atkins EM. Adaptive trajectory planning for flight management systems. 2001. Available from: www.aaai.org
  • Hovell K, Ulrich S, Bronz M. Learned multiagent real-time guidance with applications to quadrotor runway inspection. Field Robot. 2022;2(1):1105–1133. doi: 10.55417/fr.2022036.
  • Ibri S, Nourelfath M, Drias H. A multi-agent approach for integrated emergency vehicle dispatching and covering problem. Eng Appl Artif Intell. 2012;25(3):554–565 doi: 10.1016/j.engappai.2011.10.003.
  • Wang Y, Pan W, Liu K. Multi-Agent aviation search task allocation method. IOP Conference Series: Materials Science and Engineering. Bristol: Institute of Physics Publishing; 2019. doi: 10.1088/1757-899X/646/1/012058.
  • Hawe GI, Coates G, Wilson DT, et al. Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident. Eng Appl Artif Intell. 2015;46:336–345. doi: 10.1016/j.engappai.2015.06.023.
  • Bellman, Richard E., and Lotfi Asker Zadeh. “Decision-making in a fuzzy environment.” Management science 17.4 (1970): B-141.
  • Nădăban S, Dzitac S, Dzitac I. Fuzzy TOPSIS: a general view. Procedia Comput Sci. 2016;91:823–831. doi: 10.1016/j.procs.2016.07.088.
  • Saaty RW. The analytic hierarchy process-what it is and how it is used. Math Modell. 1987;9(3–5):161–176. doi: 10.1016/0270-0255(87)90473-8.
  • Patil AN. Fuzzy AHP methodology and its sole applications. 2018. Available from: www.ijmrr.com
  • Buckley JJ, Feuring T, Hayashi Y. Theory and methodology fuzzy hierarchical analysis revisited. Available from: www.elsevier.com/locate/dsw
  • Syazwan M, Soberi F, Ahmad R. Application of fuzzy AHP for setup reduction in manufacturing industry Template for Fuzzy-Analytic Hierarchy Process (F-AHP) view project integrating Single Minute Exchange of Dies (SMED) with Fuzzy Analytic Hierarchy Process (F-AHP) view project. 2016. Available from: https://www.researchgate.net/publication/318110021
  • Sathyan R, Parthiban P, Dhanalakshmi R, et al. An integrated fuzzy MCDM approach for modelling and prioritising the enablers of responsiveness in automotive supply chain using fuzzy DEMATEL, fuzzy AHP and fuzzy TOPSIS. Soft Comput. 2023;27(1):257–277. doi: 10.1007/s00500-022-07591-x.
  • Chang B, Chang CW, Wu CH. Fuzzy DEMATEL method for developing supplier selection criteria. Expert Syst Appl. 2011;38(3):1850–1858. doi: 10.1016/j.eswa.2010.07.114.
  • Baykasoğlu A, Kaplanoğlu V, Durmuşoğlu ZDU, et al. Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Syst Appl. 2013;40(3):899–907. doi: 10.1016/j.eswa.2012.05.046.
  • 51. A duzzy TOPSIS based model for safety risk.
  • Singh V, Kumar V, Singh VB. A hybrid novel fuzzy AHP-TOPSIS technique for selecting parameter-influencing testing in software development. Decision Anal J. 2023;6:100159. doi: 10.1016/j.dajour.2022.100159.
  • Yıldızbaşı A, Ünlü V. Performance evaluation of SMEs towards industry 4.0 using fuzzy group decision making methods. SN Appl Sci. 2, 355 pp (1–13) (2020). doi: 10.1007/s42452-020-2085-9.
  • Singh A, Misra SC, Kumar V, et al. Identification and ordering of safety performance indicators using fuzzy TOPSIS: a case study in indian construction company. IJQRM. 2022;39(1):77–114. doi: 10.1108/IJQRM-02-2020-0051.
  • Odeyinka OF, Okandeji AA, Sogbesan AA. A fuzzy TOPSIS model for selecting raw material suppliers in a manufacturing company. Nig J Tech. 2022;41(4):797–804. doi: 10.4314/njt.v41i4.17.

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