Görev Bazlı Uçuş Kalkış Sistemi Optimizasyonu
Yıl 2024,
, 93 - 102, 25.03.2024
Emine Yaylalı
,
Feride Şüheda Yıldız
,
Sena Koçak
Öz
Amaç: Çalışma, hava aracı ve verilen hava operasyonlarının eşleşmesi ve eşleşen çiftler için bir kalkış önceliği belirlenmesine yönelik bir sistem kurmayı amaçlamaktadır. Eşleşmeler ve kalkış sıralaması, çalışmada verilen belirli maliyetlerin toplamını en aza indirgeyecek şekilde ortaya çıkmaktadır. Ortaya çıkan sistem sayesinde, özellikle acil durumlarda operasyon - hava aracı ataması ve kalkış sırası belirlenmesi için hızlı, kullanışlı, düşük maliyetli ve verimli bir karar verme mekanizması oluşturulması hedeflenmektedir.
Method: Çalışmada 5 hava aracı ve 10 hava operasyonu içeren bir sistem oluşturuldu. Sistemin modellemesi tamsayılı programlama ile yapılırken çözüm algoritması olarak dal-sınır algoritması kullanıldı. Oluşturulan modelin çözüm algoritmasını oluşturmak için MATLAB kullanıldı.
Bulgular: Sistemin farklı koşullardaki davranışlarını ve sonuçlarını gözlemleyebilmek için hem doğal afet senaryoları şeklinde acil durum senaryoları, hem de rutin hava operasyonlarını içeren normal senaryolar tasarlanmıştır. Sistemin her senaryo için beklenen sonuçlar verdiği gözlemlenmiştir.
Orijinallik: Literatürde hava aracı kalkışı, uçuş yönetimi ile ilgili çeşitli çalışmalar olmasına rağmen operasyon tabanlı ve uçuş kalkışı birlikte kullanan çalışma sayısının yapılan araştırmalar sonucunda çok az olduğu görülmüştür. Bu sebeple görev ataması ve uçuş önceliği belirlenmesi ile ilgili yeni bir çalışma ile literatüre katkıda bulunulması hedeflenmektedir.
Destekleyen Kurum
Türk Havacılık Uzay Sanayii (TUSAŞ)
Proje Numarası
LİFT UP 2020
Kaynakça
- [1] “History of Air Traffic Control | USCA.” https://www.usca.es/en/profession/history-of-air-traffic-control/ (accessed Jul. 02, 2020).
- [2] Y. Zhang and Q. Wang, “Methods for determining unimpeded aircraft taxiing time and evaluating airport taxiing performance,” Chinese J. Aeronaut., 2017, doi: 10.1016/j.cja.2017.01.002.
- [3] H. Feuser Fernandes and C. Müller, “Optimization of the waiting time and makespan in aircraft departures: A real time non-iterative sequencing model,” J. Air Transp. Manag., 2019, doi: 10.1016/j.jairtraman.2019.101686.
- [4] A. Salehipour, “An algorithm for single- and multiple-runway aircraft landing problem,” Math. Comput. Simul., 2020, doi: 10.1016/j.matcom.2019.10.006.
- [5] V. Ho-Huu, S. Hartjes, H. G. Visser, and R. Curran, “An optimization framework for route design and allocation of aircraft to multiple departure routes,” Transp. Res. Part D Transp. Environ., 2019, doi: 10.1016/j.trd.2019.10.003.
- [6] M. Zhang, A. Filippone, and N. Bojdo, “Multi-objective optimisation of aircraft departure trajectories,” Aerosp. Sci. Technol., 2018, doi: 10.1016/j.ast.2018.05.032.
- [7] V. Karpov, A. Panin, and A. Semenov, “Calculation of Reliability of Hangars for Parking and Maintenance of Vehicles,” 2017, doi: 10.1016/j.trpro.2017.01.014.
- [8] L. Bianco and M. Bielli, “Air traffic management: Optimization models and algorithms,” J. Adv. Transp., vol. 26, no. 2, pp. 131–167, 1992, doi: 10.1002/ATR.5670260205.
- [9] H. Balakrishnan, “Control and optimization algorithms for air transportation systems,” Annu. Rev. Control, 2016, doi: 10.1016/j.arcontrol.2016.04.019.
- [10] H. Idris, “Human-Centered Automation of Air Traffic Control Operations in the Terminal Area,” MIT, 1994.
- [11] N. Raj and G. Sheela K, “Intelligent Air Traffic Control using Neural Networks,” IJSTE -International J. Sci. Technol. Eng., 2016.
- [12] J. Nogami, S. Nakasuka, and T. Tanabe, “Real-Tıme Decısıon Support For Aır Traffıc Management, Utılızıng Machıne Learnıng,” Control Eng. Pract., vol. 4, no. 8, pp. 1129–1141, 1996.
- [13] M. Schultz and S. Reitmann, “Machine learning approach to predict aircraft boarding,” Transp. Res. Part C Emerg. Technol., 2019, doi: 10.1016/j.trc.2018.09.007.
- [14] Y. Nakamura, R. Mori, H. Aoyama, and H. Jung, “Modeling of Runway Assignment Strategy by Human Controllers using Machine Learning,” 2017, doi: 10.1109/DASC.2017.8102099.
- [15] V. B. Kulkarni, “Intelligent air traffic controller simulation using artificial neural networks,” 2015 Int. Conf. Ind. Instrum. Control. ICIC 2015, pp. 1027–1031, Jul. 2015, doi: 10.1109/IIC.2015.7150897.
- [16] F. Netjasov, D. Crnogorac, and G. Pavlović, “Potential safety occurrences as indicators of air traffic management safety performance: A network based simulation model,” Transp. Res. Part C Emerg. Technol., vol. 102, no. March, pp. 490–508, 2019, doi: 10.1016/j.trc.2019.03.026.
- [17] Z. Wang, M. Liang, and D. Delahaye, “A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area,” Transp. Res. Part C Emerg. Technol., 2018, doi: 10.1016/j.trc.2018.07.019.
- [18] S. Gorripaty, Y. Liu, M. Hansen, and A. Pozdnukhov, “Identifying similar days for air traffic management,” J. Air Transp. Manag., vol. 65, pp. 144–155, 2017, doi: 10.1016/j.jairtraman.2017.06.005.
- [19] J. R. Clymer, “Induction of fuzzy rules for air traffic control,” Proc. IEEE Int. Conf. Syst. Man Cybern., vol. 2, pp. 1495–1502, 1995, doi: 10.1109/icsmc.1995.537984.
- [20] A. V Lovato, J. D. S. Silva, and E. Araujo, “Airplane Speed Control: A Fuzzy Logic Approach,” 2004, Accessed: Jun. 22, 2020. [Online]. Available: https://www.researchgate.net/publication/249783005_Airplane_Speed_Control_A_Fuzzy_Logic_Approach.
- [21] N. Idika and B. B. Baridam, “(PDF) An Intelligent Air Traffic Control System using Fuzzy Logic Model.,” Int. J. Appl. Inf. Syst., vol. 12, no. 11, 2018, Accessed: Aug. 21, 2021. [Online]. Available: https://www.researchgate.net/publication/340166411_An_Intelligent_Air_Traffic_Control_System_using_Fuzzy_Logic_Model.
- [22] K. Jenab and J. Pineau, “Automation of Air Traffic Management Using Fuzzy Logic Algorithm to Integrate Unmanned Aerial Systems into the National Airspace,” Int. J. Electr. Comput. Eng., vol. 8, no. 5, p. 3169, 2018, doi: 10.11591/ijece.v8i5.pp3169-3178.
- [23] J. Xu and G. Bailey, “The Airport Gate Assignment Problem: Mathematical model and a tabu search algorithm,” Proc. Hawaii Int. Conf. Syst. Sci., 2001, doi: 10.1109/HICSS.2001.926327.
- [24] P. Dell’Olmo and G. Lulli, “A new hierarchical architecture for air traffic management: Optimisation of airway capacity in a free flight scenario,” Eur. J. Oper. Res., 2003, doi: 10.1016/S0377-2217(01)00394-0.
- [25] X. Diao and C. H. Chen, “A sequence model for air traffic flow management rerouting problem,” Transp. Res. Part E Logist. Transp. Rev., 2018, doi: 10.1016/j.tre.2017.12.002.
- [26] D. García-Heredia, A. Alonso-Ayuso, and E. Molina, “A Combinatorial model to optimize air traffic flow management problems,” Comput. Oper. Res., vol. 112, 2019, doi: 10.1016/j.cor.2019.104768.
- [27] D. M. (Grand F. Salentiny and N. Mewes, John S. (Mayville, “Mission Prioritization and Work Order Arrangement for Unmanned Aerial Vehicles and Remotely-Piloted Vehicles,” 2016.
- [28] Y. Jiang, Z. Liao, and H. Zhang, “A collaborative optimization model for ground taxi based on aircraft priority,” Math. Probl. Eng., vol. 2013, pp. 1–9, 2013, doi: 10.1155/2013/854364.
- [29] Y. Qin, F. T. S. Chan, S. H. Chung, T. Qu, and B. Niu, “Aircraft parking stand allocation problem with safety consideration for independent hangar maintenance service providers,” Comput. Oper. Res., vol. 91, pp. 225–236, 2018, doi: 10.1016/j.cor.2017.10.001.
- [30] R. Peng, “Joint routing and aborting optimization of cooperative unmanned aerial vehicles,” Reliab. Eng. Syst. Saf., 2018, doi: 10.1016/j.ress.2018.05.004.
- [31] P. A. Bedell, “Cessna Skyhawk SP,” 1998. AOPA. https://www.aopa.org/news-and-media/all-news/1998/september/pilot/cessna-skyhawk-sp
- [32] “Cessna 172 S ( PH-HBW ) Difference Training document compared to C172R ( OOCVE ) model,” [Online]. Available: http://www.ebzr.be/wp-content/uploads/2018/07/APCK_differences_Cessna_172_S_and_R_document.pdf.
Optimization of a Mission-Based Flight Priority System
Yıl 2024,
, 93 - 102, 25.03.2024
Emine Yaylalı
,
Feride Şüheda Yıldız
,
Sena Koçak
Öz
Purpose: The purpose of this study is to develop a mission-based flight priority system that decides which aircraft would match with which airborne operation, and determines a sequence of take-off for those airplane-operation peers. Both peers and take-off orders are specified by minimizing total operation cost which includes fuel cost, waiting cost and penalty cost for missed missions. The aim of this system is to create a cost effective, fast and efficient decision-making tool for allocating operation-aircraft assignments and determining the sequence of take-off, especially in emergency cases.
Methodology: An integer programming model that minimizes the total cost are formulated. Four scenarios are designed to assess the performance of the system. The system, which includes five aircrafts and ten airborne operations, was revealed in the study. Integer programming is used while modeling the system and the Branch-and-Bound algorithm is used as the solution algorithm. The optimization algorithm was developed in MATLAB.
Findings: Both emergency scenarios and normal scenarios are maintained with the purpose of examining the behaviors and the result of the system under different conditions. It is believed that system have given the appropriate sequence and matchup for air vehicle-operation peers.
Originality: Since the integration of airplane-mission assignment and determining take-off sequence is rare in the literature, our study may be considered as a new approach. Therefore, in order to bring a new perspective, an optimization system related to the determination of flight priority and mission assignment was brought in this study.
Destekleyen Kurum
Türk Havacılık Uzay Sanayii (TUSAŞ)
Proje Numarası
LİFT UP 2020
Teşekkür
This study is supported by Turkish Aerospace LIFT UP ’20 programme. We would like to thank Turkish Aerospace (TAI) for their support and contribution to our study.
Kaynakça
- [1] “History of Air Traffic Control | USCA.” https://www.usca.es/en/profession/history-of-air-traffic-control/ (accessed Jul. 02, 2020).
- [2] Y. Zhang and Q. Wang, “Methods for determining unimpeded aircraft taxiing time and evaluating airport taxiing performance,” Chinese J. Aeronaut., 2017, doi: 10.1016/j.cja.2017.01.002.
- [3] H. Feuser Fernandes and C. Müller, “Optimization of the waiting time and makespan in aircraft departures: A real time non-iterative sequencing model,” J. Air Transp. Manag., 2019, doi: 10.1016/j.jairtraman.2019.101686.
- [4] A. Salehipour, “An algorithm for single- and multiple-runway aircraft landing problem,” Math. Comput. Simul., 2020, doi: 10.1016/j.matcom.2019.10.006.
- [5] V. Ho-Huu, S. Hartjes, H. G. Visser, and R. Curran, “An optimization framework for route design and allocation of aircraft to multiple departure routes,” Transp. Res. Part D Transp. Environ., 2019, doi: 10.1016/j.trd.2019.10.003.
- [6] M. Zhang, A. Filippone, and N. Bojdo, “Multi-objective optimisation of aircraft departure trajectories,” Aerosp. Sci. Technol., 2018, doi: 10.1016/j.ast.2018.05.032.
- [7] V. Karpov, A. Panin, and A. Semenov, “Calculation of Reliability of Hangars for Parking and Maintenance of Vehicles,” 2017, doi: 10.1016/j.trpro.2017.01.014.
- [8] L. Bianco and M. Bielli, “Air traffic management: Optimization models and algorithms,” J. Adv. Transp., vol. 26, no. 2, pp. 131–167, 1992, doi: 10.1002/ATR.5670260205.
- [9] H. Balakrishnan, “Control and optimization algorithms for air transportation systems,” Annu. Rev. Control, 2016, doi: 10.1016/j.arcontrol.2016.04.019.
- [10] H. Idris, “Human-Centered Automation of Air Traffic Control Operations in the Terminal Area,” MIT, 1994.
- [11] N. Raj and G. Sheela K, “Intelligent Air Traffic Control using Neural Networks,” IJSTE -International J. Sci. Technol. Eng., 2016.
- [12] J. Nogami, S. Nakasuka, and T. Tanabe, “Real-Tıme Decısıon Support For Aır Traffıc Management, Utılızıng Machıne Learnıng,” Control Eng. Pract., vol. 4, no. 8, pp. 1129–1141, 1996.
- [13] M. Schultz and S. Reitmann, “Machine learning approach to predict aircraft boarding,” Transp. Res. Part C Emerg. Technol., 2019, doi: 10.1016/j.trc.2018.09.007.
- [14] Y. Nakamura, R. Mori, H. Aoyama, and H. Jung, “Modeling of Runway Assignment Strategy by Human Controllers using Machine Learning,” 2017, doi: 10.1109/DASC.2017.8102099.
- [15] V. B. Kulkarni, “Intelligent air traffic controller simulation using artificial neural networks,” 2015 Int. Conf. Ind. Instrum. Control. ICIC 2015, pp. 1027–1031, Jul. 2015, doi: 10.1109/IIC.2015.7150897.
- [16] F. Netjasov, D. Crnogorac, and G. Pavlović, “Potential safety occurrences as indicators of air traffic management safety performance: A network based simulation model,” Transp. Res. Part C Emerg. Technol., vol. 102, no. March, pp. 490–508, 2019, doi: 10.1016/j.trc.2019.03.026.
- [17] Z. Wang, M. Liang, and D. Delahaye, “A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area,” Transp. Res. Part C Emerg. Technol., 2018, doi: 10.1016/j.trc.2018.07.019.
- [18] S. Gorripaty, Y. Liu, M. Hansen, and A. Pozdnukhov, “Identifying similar days for air traffic management,” J. Air Transp. Manag., vol. 65, pp. 144–155, 2017, doi: 10.1016/j.jairtraman.2017.06.005.
- [19] J. R. Clymer, “Induction of fuzzy rules for air traffic control,” Proc. IEEE Int. Conf. Syst. Man Cybern., vol. 2, pp. 1495–1502, 1995, doi: 10.1109/icsmc.1995.537984.
- [20] A. V Lovato, J. D. S. Silva, and E. Araujo, “Airplane Speed Control: A Fuzzy Logic Approach,” 2004, Accessed: Jun. 22, 2020. [Online]. Available: https://www.researchgate.net/publication/249783005_Airplane_Speed_Control_A_Fuzzy_Logic_Approach.
- [21] N. Idika and B. B. Baridam, “(PDF) An Intelligent Air Traffic Control System using Fuzzy Logic Model.,” Int. J. Appl. Inf. Syst., vol. 12, no. 11, 2018, Accessed: Aug. 21, 2021. [Online]. Available: https://www.researchgate.net/publication/340166411_An_Intelligent_Air_Traffic_Control_System_using_Fuzzy_Logic_Model.
- [22] K. Jenab and J. Pineau, “Automation of Air Traffic Management Using Fuzzy Logic Algorithm to Integrate Unmanned Aerial Systems into the National Airspace,” Int. J. Electr. Comput. Eng., vol. 8, no. 5, p. 3169, 2018, doi: 10.11591/ijece.v8i5.pp3169-3178.
- [23] J. Xu and G. Bailey, “The Airport Gate Assignment Problem: Mathematical model and a tabu search algorithm,” Proc. Hawaii Int. Conf. Syst. Sci., 2001, doi: 10.1109/HICSS.2001.926327.
- [24] P. Dell’Olmo and G. Lulli, “A new hierarchical architecture for air traffic management: Optimisation of airway capacity in a free flight scenario,” Eur. J. Oper. Res., 2003, doi: 10.1016/S0377-2217(01)00394-0.
- [25] X. Diao and C. H. Chen, “A sequence model for air traffic flow management rerouting problem,” Transp. Res. Part E Logist. Transp. Rev., 2018, doi: 10.1016/j.tre.2017.12.002.
- [26] D. García-Heredia, A. Alonso-Ayuso, and E. Molina, “A Combinatorial model to optimize air traffic flow management problems,” Comput. Oper. Res., vol. 112, 2019, doi: 10.1016/j.cor.2019.104768.
- [27] D. M. (Grand F. Salentiny and N. Mewes, John S. (Mayville, “Mission Prioritization and Work Order Arrangement for Unmanned Aerial Vehicles and Remotely-Piloted Vehicles,” 2016.
- [28] Y. Jiang, Z. Liao, and H. Zhang, “A collaborative optimization model for ground taxi based on aircraft priority,” Math. Probl. Eng., vol. 2013, pp. 1–9, 2013, doi: 10.1155/2013/854364.
- [29] Y. Qin, F. T. S. Chan, S. H. Chung, T. Qu, and B. Niu, “Aircraft parking stand allocation problem with safety consideration for independent hangar maintenance service providers,” Comput. Oper. Res., vol. 91, pp. 225–236, 2018, doi: 10.1016/j.cor.2017.10.001.
- [30] R. Peng, “Joint routing and aborting optimization of cooperative unmanned aerial vehicles,” Reliab. Eng. Syst. Saf., 2018, doi: 10.1016/j.ress.2018.05.004.
- [31] P. A. Bedell, “Cessna Skyhawk SP,” 1998. AOPA. https://www.aopa.org/news-and-media/all-news/1998/september/pilot/cessna-skyhawk-sp
- [32] “Cessna 172 S ( PH-HBW ) Difference Training document compared to C172R ( OOCVE ) model,” [Online]. Available: http://www.ebzr.be/wp-content/uploads/2018/07/APCK_differences_Cessna_172_S_and_R_document.pdf.