Research Article
YAPAY SİNİR AĞLARI VE ADAPTİF NÖROBULANIK SİSTEMLER İLE 3. İSTANBUL HAVALİMANI TALEP TAHMİNİ VE TÜRK HAVA YOLLARI İÇ HAT FİLO OPTİMİZASYONU
Abstract
Bu çalışmanın amacı, İstanbul Atatürk Havalimanı’na
ikame olarak inşa edilen 3. İstanbul havalimanının gelecek yıllardaki yolcu ve
yük talebini, İstanbul Atatürk Havalimanının geçmiş dönem verileri ile Yapay
Sinir Ağları (YSA) ve Adaptif Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi (ANFIS)
yöntemleri kullanılarak tahmin etmek, muhtemel kapasiteye ışık tutmak ve ön
görülen operasyon hacmini gerçekleştirebilmek adına muhtemel uçak filosunu
finansal ve fiziksel kısıtlar kullanılarak senaryolar altında
planlayabilmektir. Çalışmanın verileri Türk İstatistik Kurumu (TÜİK) tarafından
derlenmiş olup normalizasyon sürecine tabi tutulmuştur. Hata ölçüm metodu
olarak Kare Kök Ortalama Hata (RMSE) ve Hata Kareleri Toplamı (SSE) karşılaştırmalı
olarak kullanılmış ve performansları değerlendirilmiştir. Çalışmanın bulguları
3. Havalimanının önümüzdeki senelerdeki tahmini yolcu ve yük değerlerinin yanında
muhtemel talebe karşılık verip veremeyeceği ve havalimanının performans
karakteristiği hakkında önemli bilgiler içermektedir.
References
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Year 2019,
Volume: 30 Issue: 2, 141 - 156, 31.10.2019
Abstract
References
- 1. Blegur, F.M.A., Bakhtiar, T., Aman, A.: Scenarios for fleet assignment: a case study at Lion Air. IOSR Journal of Mathematics 10(5) Ver. I, 64-68 (2014)
- 2. Doganis, R. (2009). Flying off course IV: airline economics and marketing. Routledge.
- 3. Blinova, T. O. (2007). Analysis of possibility of using neural network to forecast passenger traffic flows in Russia. Aviation, 11(1), 28-34.
- 4. Srisaeng, P., Baxter, G. S., & Wild, G. (2015). Forecasting demand for low cost carriers in Australia using an artificial neural network approach. Aviation, 19(2), 90-103.
- 5. Abara, J. , 1989. Applying integer linear programming to the fleet assignment prob- lem. Interfaces (Providence) 19 (4), 20–28 .
- 6. Clarke, J-P., 2003. The role of advanced air traffic management in reducing the impact of aircraft noise and enabling aviation growth. J. Air Transp. Manage. 9, 161–165
- 7. Prats, X., Puig, V., Quevedo, J., Nejjari, F., 2010a. Lexicographic optimisation for optimal departure aircraft trajectories. Aerosp. Sci. Technol. 14, 26–37.
- 8. Prats, X., Puig, V., Quevedo, J., Nejjari, F., 2010b. Multi-objective optimisation for aircraft departure trajectories minimising noise annoyance. Transp. Res. Part C 18, 975–989.
- 9. Prats, X., Puig, V., Quevedo, J., 2011. A multi-objective optimization strategy for designing aircraft noise abatement procedures. Case study at Girona airport. Transp. Res. Part D 16, 31–41.
- 10. Visser, H.G., 2005. Generic and site-specific criteria in the optimization of noise abatement trajectories. Transp. Res. Part D 10, 405–419.
- 11. Hsu, C.I., Lin, P.H., 2005. Performance assessment for airport noise charge policies and airline network adjustment response. Transp. Res. Part D 10, 281–304.
- 12. Hsu, C.I., Li, H.C., Liu, S.M., Chao, C.C., 2011. Aircraft replacement scheduling: a dynamic programming approach. Transp. Res. Part E 47, 41–60.
- 13. Hsu, C.I., Chao, C.C., Huang, P.S., 2011. Fleet dry/wet lease planning of airlines on strategic alliances. Transportmetrica 9, 603–628.
- 14. Gomes, L.F.A.M., Fernandes, J.E.M., Soares de Mello, J.C.C.B., 2014. A fuzzy stochastic approach to the multicriteria selection of an aircraft for regional chartering. J. Adv. Transp. 48, 223e237.
- 15. Naumann, M. , Suhl, L. , Friedemann, M. , 2012. A stochastic programming model for integrated planning of re-fleeting and financial hedging under fuel price and demand uncertainty. Proc. Soc. Behav. Sci. 54, 47–55 .
- 16. Y. Ozdemir, H. Basligil, and B. Sarsenov, A large scale integer linear programming to the daily fleet assignment problem: a case study in Turkey, Procedia-Social and Behavioral Sciences, 62, 2012, 849-853.
- 17. Efendigil, T., & Eminler, Ö. E. (2017). Havacılık Sektöründe Talep Tahminin Önemi: Yolcu Talebi Üzerine Bir Tahmin Modeli. Journal of Yaşar University, 12(48), 14-30.
- 18. Jang, J.S.R., "ANFIS Adaptive-Network-Based Fuzzy Inference Systems," Man, And Cybernetics, Vol. 23, No. 3, May, 665-685 1993
- 19. Ying, L. C., & Pan, M. C. (2008). Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversion and Management, 49(2), 205-211.
- 20. Firat, M., Turan, M. E., & Yurdusev, M. A. (2009). Comparative analysis of fuzzy inference systems for water consumption time series prediction. Journal of hydrology, 374(3-4), 235-241.
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- 26. http://www.igairport.com/documents/Kurumsal_Yayinlar/analiziistabulyenihavalimaniekonomiketkianalizi.pdf /22.07.2019 Erişim Tarihi
- 27. Kolarik, T., & Rudorfer, G. (1994, August). Time series forecasting using neural networks. In ACM Sigapl Apl Quote Quad (Vol. 25, No. 1, pp. 86-94). ACM.
- 28. Karlaftis, M. G., Zografos, K. G., Papastavrou, J. D., & Charnes, J. M. (1996). Methodological framework for air-travel demand forecasting. Journal of Transportation Engineering, 122(2), 96-104.]
- 29. Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks:: The state of the art. International journal of forecasting, 14(1), 35-62.
- 30. Kihoro, J., Otieno, R. O., & Wafula, C. (2004). Seasonal time series forecasting: A comparative study of ARIMA and ANN models
- 31. Zandieh, M., Azadeh, A., Hadadi, B., & Saberi, M. (2009). Application of artificial neural networks for airline number of passenger estimation in time series state. Journal of Applied Sciences, 9(6), 1001-1013.
- 32. Hamzaçebi, C. (2008). Improving artificial neural networks’ performance in seasonal time series forecasting. Information Sciences, 178(23), 4550-4559.
There are 32 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Industrial Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | October 31, 2019 |
| Acceptance Date | October 8, 2019 |
| Published in Issue | Year 2019 Volume: 30 Issue: 2 |
