FEATURE ANALYSIS FOR MULTI-CRITERIA RATING VALUES OF AIRLINE COMPANIES

Öz

The development of information and communication technologies offers the possibility of collecting and sharing customer views, comments and ratings about products and services over the Internet. Customers generally make these evaluations based on multiple criteria. This study uses such data recorded on Skytrax to analyse the performance of leading airlines. It does so using the a multicriteria decision making technique (Promethee II), and the criteria weight values required for the Promethee II method are obtained from a Multi-Layer Perceptron (MLP), an artificial neural network method. According to the results obtained, ANA airline has shown improvements in the years and moved up to the top, while the ranking of United airline within two years has not changed. The paper provides details of the technique and graphically presents results to highlight where airlines possess advantages over their competitors.

Anahtar Kelimeler

• Multi Criteria Decision Making,Artificial Neural Network,Multi-layer Perceptron,Promethee II,Airline Companies

HAVAYOLU FİRMALARININ ÇOK KRİTERLİ OY DEĞERLERİ İÇİN NİTELİK ANALİZİ

Öz

Bilgi ve iletişim teknolojilerinin gelişmesi, internette yer alan servisler ve ürünler hakkında müşterinin bakış açısı, yorumları ve oy değerlerinin paylaşılmasına ve toplanmasına imkân sağlamıştır. Müşteriler, bu değerlendirmeleri çoklu kriterlere dayanarak gerçekleştirmektedir. Bu çalışmada, havayolu firmalarının performans analizi için Skytrax’ da yer alan veriler kullanılmıştır. Çok kriterli karar verme teknikleri kullanılarak yapılan bu çalışmada, Promethee II için gerekli olan ağırlık değerleri, bir yapay sinir ağları modeli olan Çok Katmanlı Algılayıcı (MLP) ile elde edilmiştir. Elde edilen sonuçlarda ANA havayolu firmasının yıllar içerisinde gelişmeler gösterip üst sıralara taşınırken, United havayolu firmasının iki yıl içerisindeki sıralamasında herhangi bir değişiklik gözlenmemiştir. Bu makalede kullanılan tekniklerin detayları verilirken, elde edilen sonuçlarda havayolu firmaları için rekabette sağladığı avantajlar vurgulanmıştır.

Anahtar Kelimeler

• Çok Kriterli Karar Verme,Yapay Sinir Ağları,Çok Katmanlı Algılayıcı,Promethee II,Havayolu Firmaları

Kaynakça

1. Barros, C., P. and Wanke, P., 2015. An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management, 44-45, 90-102.
2. Bongo, M.F., Alimpangog, K.M.S., Loar, J.F., Montefalcon, J.A., Ocampo, L.A., 2017. An application of DEMATEL-ANP and PROMETHEE II approach for air traffic controllers’ workload stress problem: A case of Mactan Civil Aviation Authority of the Philippines. Journal of Air Transport Management.
3. Brans, J-P., Mareschal, B., 2005. Promethee Methods in: Multipler criteria decision analysis: State of the art surveys. International Series in Operations Research and Management Science, New York, Vol. 78.
4. Brans, J-P. and Vincke, P., 1985. A preference ranking organization method: The PROMETHEE method for MCDM. Management Science, Vol. 31(6), 647-656.
5. Brans, J-P., Vinckle, P., and Mareschal, B., 1986. How to select and how to rank projects: The PROMETHEE Method. European Journal of Operational Research, Vol. 24, 228-238.
6. Dagdeviren, M., and Eraslan, E., 2008. PROMETHEE sıralama yöntemi ile tedarikçi seçimi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Vol. 23(1), 69-75.
7. Feng, C.M. and Wang, R. T., 2000. Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 133-142.
8. Gardner, M. W. and Dorling, S. R., 1998. Artificial Neural Networks (the Multilayer Perceptron) a review of applications in the atmospheric sciences. Atmospheric Environment, Vol. 32, No. 14-15, 2627-2636.

9. Gourdin, K., 1998. Bringing quality back to commercial travel. Transport Journal, 23-29.
10. Jarvis, R.A. and Patrick, E.A., 1973. Clustering using a similarity measure based on shared near neighbors. IEEE Transaction on Computers, C22, 1025-1034.
11. Kaya, T., 2017. Hybrid approach on airline companies. 8th International Advanced Technologies Symposium, IATS’17, Elazığ.
12. Kurtulmusoglu, F.B., Can, G.F. and Tolon M., 2016. Avoice in the skies: Listening to airline passenger preferences. Journal of Air Transport Management, Vol.57, 130-137.
13. Lacic, E., Kowald, D., Lex, E., 2016. High enough? Explaining and predicting traveler satisfaction using airline review. Proceedings of the 27th ACM Conference on Hypertext and Social Media, Halifax, Canada.
14. Liou, J.J.H., Hsu, C-C., Yeh, W-C., 2011. Lin, R.H., Using a modified grey relation method for improving airline service quality. Tourism Management, Vol. 32, 1381-1388.
15. Liou, J.J.H. and Tzeng, G.H., 2010. A dominance-based rough set approach to customer behaviour in the airline market. Information Sciences, 180, 2230-2238.
16. Miranda, H. S. and Henriques, R. (2013). Building clusters for CRM strategies by mining airlines customer data. 8th Iberian Conference on Information Systems and Technologies (CISTI), 1-5.
17. Oz, Y. and Koksal, C.D., 2016. Analyzing efficiencies and Total Factor Productivities of Star Alliance Member Airlines. The Online Journal of Science and Technology, Vol. 6, Issue 1, 5-12.
18. Perezgonzalez, J. D. and Gilbey, A., 2011. Predicting skytrax airport rankings from customer reviews. Journal of Airport Management, 5, 336.
19. Senkayas H., Hekimoğlu H., 2013. Çok kriterli tedarikçi seçimi problemine PROMETHEE yöntemi uygulaması. Verimlilik Dergisi, 63-80.
20. Strehl, A., Ghosh, J. and Mooney, R., 2000. Impact of similarity measures on web-page clustering. Workshop on Artificial Intelligence for Web Search (AAAI 2000), 58-64.
21. Tsaur, S.H., Chang, T.Y. and Yen, C.H., 2002. The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 107-115.
22. Vassilev, V., Genova, K, and Vassileva M., 2005. A Brief survey of multicriteria decision making methods and software systems. Cybernetics and Information Technologies, Vol. 5, No 1, 3-13.
23. Vela, M. R., and Garcia, E. M., 2010. A segmentation analysis and segments profile of budget air travelers. Cuadernos de Turismo, 26, 235-25.
24. Wang, H., Wang, W., Yang, J. and Yu, P.S., 2002. Clustering by pattern similarity in large data sets. Special Interest Group on Management of Data (SIGMOD 2002), Madison, Wisconsin, USA, 394-405. Wang, R., Ho, C-T., Feng, C-M. and Yang, Y-K., 2004. A comparative analysis of the operational performance of Taiwan’s major airports. Journal of Air Transport Management, 353-360.