Year 2025,
Volume: 26 Issue: 2, 161 - 178, 25.06.2025
Gülseren Yeşil
,
Özlem Şahin
References
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[1] Moser I. Scheduling aircraft landings dynamically using stochastic and deterministic elements. Int J Inf Technol Intell Comput 2007; 2: 1-21.
-
[2] Brentnall AR, Cheng RC. Some effects of aircraft arrival sequence algorithms. J Oper Res Soc 2009; 60: 962-972.
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[3] Çeçen RK, Cetek C, Kaya O. Aircraft sequencing and scheduling in TMAs under wind direction uncertainties. Aeronaut J 2020; 124: 1896-1912.
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[4] Dönmez K. A stochastic sequence planning model for the runways with multiple exits. Akıllı Ulaşım Sist Uyg Derg 2022; 5: 89-101.
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[5] Liang M. Aircraft route network optimization in terminal maneuvering area. Univ Paul Sabatier Toulouse 3 2018.
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[6] Dear RG. The dynamic scheduling of aircraft in the near terminal area. Cambridge, Mass: Flight Transportation Laboratory, Massachusetts Institute of Technology, 1976.
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[7] Ikli S, Mancel C, Mongeau M, Olive X, Rachelson E. The aircraft runway scheduling problem: A survey. Comput Oper Res 2021; 132: 105336.
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[8] Ören A, Koçyiğit Y. Unmanned aerial vehicles landing sequencing modelling via fuzzy logic/ İnsansız hava araçları iniş sıralamasının bulanık mantık modellemesi. Celal Bayar Univ J Sci 2016; 12: 1-21.
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[9] Pratiwi W, Sofwan A, Setiawan I. Implementation of fuzzy logic method for automation of decision making of Boeing aircraft landing. IAES Int J Artif Intell 2021; 10: 545-555.
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[10] Ntakolia C, Lyridis DV. A n− D ant colony optimization with fuzzy logic for air traffic flow management. Oper Res 2022; 22: 5035-5053.
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[11] Bongo MF, Seva RR. Evaluating the performance-shaping factors of air traffic controllers using fuzzy DEMATEL and fuzzy BWM approach. Aerospace 2023; 10: 252-262.
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[12] Chikha P, Skorupski J. The risk of an airport traffic accident in the context of the ground handling personnel performance. J Air Transp Manag 2022; 105: 102295.
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[13] Kolotusha V, Shmelova T, Bondarev D. Multi-criterion evaluation of the criteria of the information model conformity of the air traffic controller simulator to the real system. Int Workshop Adv Civ Aviat Syst Dev 2024; 364-384.
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[14] Lee WK. Risk assessment modeling in aviation safety management. J Air Transp Manag 2006; 12: 267-273.
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[15] Siergiejczyk M, Krzykowska K, Rosiński A. Reliability assessment of cooperation and replacement of surveillance systems in air traffic. Adv Intell Syst Comput 2014; 286: 403-411.
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[16] Ross TJ. Fuzzy logic with engineering applications. (Second Edition). England: John Wiley and Sons, 2004.
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[17] Ali OAM, Ali AY, Sumait BS. Comparison between the effects of different types of membership functions on fuzzy logic controller performance. Int J 2015; 76: 76-83.
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[18] Dumitrescu C, Ciotirnae P, Vizitiu C. Fuzzy logic for intelligent control system using soft computing applications. Sensors 2021; 21(8): 2617.
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[19] Yahia NB, Bellamine N, Ghezala HB. Integrating fuzzy case-based reasoning and particle swarm optimization to support decision making. Int J Comput Sci Issues 2012; 9(3): 117.
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[20] Kıyak E. Flight Control Using Fuzzy Logic Method. Eskişehir: Anadolu Üniv Fen Bil Enst, 2003.
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[21] Ceruto T, Lapeira O, Tonch A, Plant C, Espin R, Rosete A. Mining medical data to obtain fuzzy predicates. In: Information Technology in Bio-and Medical Informatics: 5th International Conference, ITBAM 2014, Munich, Germany, September 2, 2014; Proceedings 5. Springer International Publishing; 2014. p. 103-117.
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[22] Katircioglu F, Sen MOY, Kelek MM, Koyuncu I. FPGA-Based Design of Gaussian Membership Function for Real-Time Fuzzy Logic Applications. In: International Multidisciplinary Congress of Eurasia 2018; 2018. p. 33-39.
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[23] Sabri N, Aljunid SA, Salim MS, Badlishah RB, Kamaruddin R, Malek MA. Fuzzy inference system: Short review and design. Int Rev Autom Control 2013; 6(4): 441-449.
-
[24] Khademi Hamidi J, Shahriar K, Rezai B, Bejari H. Application of fuzzy set theory to rock engineering classification systems: an illustration of the rock mass excavability index. Rock Mech Rock Eng 2010; 43: 335-350.
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[25] Karasakal O. Online rule weighting methods for fuzzy pid controllers. İstanbul Tek Üniv Fen Bil Enst 2012.
AIRCRAFT SEQUENCING WITH FUZZY LOGIC METHOD
Year 2025,
Volume: 26 Issue: 2, 161 - 178, 25.06.2025
Gülseren Yeşil
,
Özlem Şahin
Abstract
The growth in the demand for air transport causes an increase in air traffic. In this case, more air traffic needs to be carried out safely, regularly and quickly. It is important to correctly and fairly order the landing traffic in order to reduce the delaying, especially in the air due to heavy traffic.
In this study, it is aimed to sequence the arrival traffic with fuzzy logic method. Speed, distance, altitude parameters were used for sequencing. Traffic data was collected from the Air Traffic Simulation Laboratory at Eskişehir Technical University’s Air Traffic Control Department.
A comparative analysis was conducted between the current arrival traffic sequencing within the simulation area and the sequencing results derived from the fuzzy logic method. The findings indicate a significant overlap between both traffic ranking outcomes. This research contributes to the existing literature by demonstrating the application of the fuzzy logic method in the field of air traffic control.
Supporting Institution
This study was supported by Eskişehir Technical University Scientific Research Project Commission
References
-
[1] Moser I. Scheduling aircraft landings dynamically using stochastic and deterministic elements. Int J Inf Technol Intell Comput 2007; 2: 1-21.
-
[2] Brentnall AR, Cheng RC. Some effects of aircraft arrival sequence algorithms. J Oper Res Soc 2009; 60: 962-972.
-
[3] Çeçen RK, Cetek C, Kaya O. Aircraft sequencing and scheduling in TMAs under wind direction uncertainties. Aeronaut J 2020; 124: 1896-1912.
-
[4] Dönmez K. A stochastic sequence planning model for the runways with multiple exits. Akıllı Ulaşım Sist Uyg Derg 2022; 5: 89-101.
-
[5] Liang M. Aircraft route network optimization in terminal maneuvering area. Univ Paul Sabatier Toulouse 3 2018.
-
[6] Dear RG. The dynamic scheduling of aircraft in the near terminal area. Cambridge, Mass: Flight Transportation Laboratory, Massachusetts Institute of Technology, 1976.
-
[7] Ikli S, Mancel C, Mongeau M, Olive X, Rachelson E. The aircraft runway scheduling problem: A survey. Comput Oper Res 2021; 132: 105336.
-
[8] Ören A, Koçyiğit Y. Unmanned aerial vehicles landing sequencing modelling via fuzzy logic/ İnsansız hava araçları iniş sıralamasının bulanık mantık modellemesi. Celal Bayar Univ J Sci 2016; 12: 1-21.
-
[9] Pratiwi W, Sofwan A, Setiawan I. Implementation of fuzzy logic method for automation of decision making of Boeing aircraft landing. IAES Int J Artif Intell 2021; 10: 545-555.
-
[10] Ntakolia C, Lyridis DV. A n− D ant colony optimization with fuzzy logic for air traffic flow management. Oper Res 2022; 22: 5035-5053.
-
[11] Bongo MF, Seva RR. Evaluating the performance-shaping factors of air traffic controllers using fuzzy DEMATEL and fuzzy BWM approach. Aerospace 2023; 10: 252-262.
-
[12] Chikha P, Skorupski J. The risk of an airport traffic accident in the context of the ground handling personnel performance. J Air Transp Manag 2022; 105: 102295.
-
[13] Kolotusha V, Shmelova T, Bondarev D. Multi-criterion evaluation of the criteria of the information model conformity of the air traffic controller simulator to the real system. Int Workshop Adv Civ Aviat Syst Dev 2024; 364-384.
-
[14] Lee WK. Risk assessment modeling in aviation safety management. J Air Transp Manag 2006; 12: 267-273.
-
[15] Siergiejczyk M, Krzykowska K, Rosiński A. Reliability assessment of cooperation and replacement of surveillance systems in air traffic. Adv Intell Syst Comput 2014; 286: 403-411.
-
[16] Ross TJ. Fuzzy logic with engineering applications. (Second Edition). England: John Wiley and Sons, 2004.
-
[17] Ali OAM, Ali AY, Sumait BS. Comparison between the effects of different types of membership functions on fuzzy logic controller performance. Int J 2015; 76: 76-83.
-
[18] Dumitrescu C, Ciotirnae P, Vizitiu C. Fuzzy logic for intelligent control system using soft computing applications. Sensors 2021; 21(8): 2617.
-
[19] Yahia NB, Bellamine N, Ghezala HB. Integrating fuzzy case-based reasoning and particle swarm optimization to support decision making. Int J Comput Sci Issues 2012; 9(3): 117.
-
[20] Kıyak E. Flight Control Using Fuzzy Logic Method. Eskişehir: Anadolu Üniv Fen Bil Enst, 2003.
-
[21] Ceruto T, Lapeira O, Tonch A, Plant C, Espin R, Rosete A. Mining medical data to obtain fuzzy predicates. In: Information Technology in Bio-and Medical Informatics: 5th International Conference, ITBAM 2014, Munich, Germany, September 2, 2014; Proceedings 5. Springer International Publishing; 2014. p. 103-117.
-
[22] Katircioglu F, Sen MOY, Kelek MM, Koyuncu I. FPGA-Based Design of Gaussian Membership Function for Real-Time Fuzzy Logic Applications. In: International Multidisciplinary Congress of Eurasia 2018; 2018. p. 33-39.
-
[23] Sabri N, Aljunid SA, Salim MS, Badlishah RB, Kamaruddin R, Malek MA. Fuzzy inference system: Short review and design. Int Rev Autom Control 2013; 6(4): 441-449.
-
[24] Khademi Hamidi J, Shahriar K, Rezai B, Bejari H. Application of fuzzy set theory to rock engineering classification systems: an illustration of the rock mass excavability index. Rock Mech Rock Eng 2010; 43: 335-350.
-
[25] Karasakal O. Online rule weighting methods for fuzzy pid controllers. İstanbul Tek Üniv Fen Bil Enst 2012.