Uluslararası Havacılık Kurumlarının Operasyonel Performansının GSBTOPSIS Yöntemi ile Analizi

Year 2024, Volume: 58 Issue: 1, 91 - 102, 31.01.2024

Mustafa Özdemir

https://doi.org/10.51551/verimlilik.1296157

Abstract

Amaç: Çalışmanın temel amacı havacılık alanında kullanılan operasyonel performans değişkenlerinin önem düzeylerine göre derecelendirilmesi ve uluslararası havacılık işletmelerinin operasyonel performanslarının ölçülmesidir.
Yöntem: Kriter ağırlıklandırmada ve performans ölçümünde nispeten yeni ve havacılık alanında kullanım örneği bulunmayan maksimum sapma ile genişletilmiş sezgisel bulanık TOPSIS yöntemi kullanılmıştır.
Bulgular: Araştırma sonuçlarına göre operasyonel performans değerlendirmesinde kullanılan değişkenler içerisinde kontrol edilen toplam havaalanı hareketliliği en yüksek önem ağırlığına sahip kriter olduğu anlaşılmaktadır. En iyi operasyonel performans sıralamasında DSNA (Fransa), ENAIRE (İspanya) ve DHMİ (Türkiye) ilk üç sırada yer almaktadır.
Özgünlük: EUROCONTROL tarafından sunulan operasyonel performans değişkenleri ilk kez genişletilmiş sezgisel bulanık TOPSIS yöntemi ile test edilmiştir.

Keywords

Havacılık, Operasyonel Performans, Sezgisel Bulanık Mantık, GSBTOPSIS, ÇKKV

Analysis of the Operational Performance of International Aviation Institutions with the EIFTOPSIS Method

Abstract

Purpose: The main goal of this study is to rank the operational performance variables used in aviation according to their importance levels and to measure the operational performance of international aviation institutions.
Methodology: An extended intuitionistic fuzzy TOPSIS method with maximum deviation is used in criterion weighting and performance measurement, which is relatively new and has no use case in aviation.
Findings: According to the results obtained in the study, it is understood that the total airport movements controlled among the variables used in the operational performance evaluation are the criterion with the highest importance. DSNA (France), ENAIRE (Spain), and DHMI (Türkiye) are in the top three in the best operational performance rankings.
Originality: The operational performance variables offered by EUROCONTROL have been tested for the first time with the extended intuitionistic fuzzy TOPSIS method.

Keywords

Aviation, Operational Performance, Intuitionistic Fuzzy Logic, EIFTOPSIS, MCDM

References

• Anbanandam, R., Banwet, D.K. and Shankar, R. (2011). “Evaluation of Supply Chain Collaboration: a Case of Apparel Retail Industry in India”, International Journal of Productivity and Performance Management, 60(2), 82-98.
• Arnaldo, R.M., Comendador, V.F.G., Barragan, R. and Pérez, L. (2014). “European Air Navigation Service Providers Efficiency Evaluation Through Data Envelopment Analysis (DEA)”, In 29th Congress of the International Council of the Aeronautical Sciences (CICAS2014), Petersburg, Russia,1-7.
• Assaf, A.G. and Josiassen, A. (2011). “The Operational Performance of UK Airlines: 2002‐2007”, Journal of Economic Studies, 38(1), 5-16.
• Atanassov, K.T. (1986). “Intuitionistic Fuzzy Sets”, Fuzzy Sets and Systems, (20). 87-96.
• Bae, K., Gupta, A. and Mau, R. (2021). “Comparative Analysis of Airline Financial and Operational Performances: A Fuzzy AHP and TOPSIS Integrated Approach”, Decision Science Letters, 10(3), 361-374.
• Bai, C., Dhavale, D. and Sarkis, J. (2014). “Integrating Fuzzy C-Means and TOPSIS for Performance Evaluation: An Application and Comparative Analysis”. Expert Systems with Applications, 41(9), 4186-4196.
• Bai, C. and Sarkis, J. (2013). “Green Information Technology Strategic Justification and Evaluation”, Information Systems Frontiers, (15), 831-847.
• Bakir, M., Akan, Ş., Kiraci, K., Karabasevic, D., Stanujkic, D. and Popovic, G. (2020). “Multiple-Criteria Approach of the Operational Performance Evaluation in the Airline Industry: Evidence from the Emerging Markets”, Romanian Journal of Economic Forecasting, 23(2), 149-172.
• Barbot, C., Costa, Á. and Sochirca, E. (2008). “Airlines Performance in the New Market Context: A Comparative Productivity and Efficiency Analysis”, Journal of Air Transport Management, 14(5), 270-274.
• Barros, C.P. and Dieke, P.U. (2008). “Measuring the Economic Efficiency of Airports: A Simar-Wilson Methodology Analysis”, Transportation Research Part E: Logistics and Transportation Review, 44(6), 1039-1051.
• Barros, C.P. and Peypoch, N. (2009). “An Evaluation of European Airlines’ Operational Performance”, International Journal of Production Economics, 122(2), 525-533.
• 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, 90-102.
• Belobaba, P.P. (2015). “Airline Revenue Management”, The global Airline Industry, Editor: Belobaba, P., Odoni, A. and Barnhart, C., John Wiley and Sons, United Kingdom, 99-126.
• Belton, V. and Stewart, T. (2002). “Multiple Criteria Decision Analysis: An Integrated Approach”, Kluwer Academic Publishers.
• Bilotkach, V., Gitto, S., Jovanović, R., Mueller, J. and Pels, E. (2015). “Cost-Efficiency Benchmarking of European Air Navigation Service Providers”, Transportation Research Part A: Policy and Practice, 77, 50-60.
• Boutkhoum, O., Hanine, M. and Bendarag, A. (2018). “A Comparative Analysis Approach Based on Fuzzy AHP, TOPSIS and PROMETHEE for the Selection Problem of GSCM Solutions”, International Journal of Computer and Systems Engineering, 12(10), 859-870.
• Buyle, S., Dewulf, W., Onghena, E., Meersman, H. and Van de Voorde, E. (2018). “Economies of Scale and Cost Complementarities in the European ANS Industry: A Multiproduct Translog Cost Function Approach”, Proceedings of 22nd ATRS World Conference, Seoul, Korea, 2-5 July 2018,1-13.
• Chen, M.F. and Tzeng, G.H. (2004). “Combining Grey Relation and TOPSIS Concepts for Selecting an Expatriate Host Country”, Mathematical and Computer Modelling, 40(13), 1473-1490.
• Dožić, S. (2019). “Multi-Criteria Decision Making Methods: Application in the Aviation Industry”, Journal of Air Transport Management, 79, 101683.
• EUROCONTROL (2021). “Performance Review Report an Assessment of Air Traffic Management in Europe Performance Review Commission”, https://www.eurocontrol.int/publication/performance-review-report-prr-2021 (Access Date: 10.04.2023).
• Färe, R., Grosskopf, S. and Sickles, R.C. (2007). “Productivity? of US Airlines After Deregulation”, Journal of Transport Economics and Policy (JTEP), 41(1), 93-112.
• Garg, C.P. (2016). “A Robust Hybrid Decision Model for Evaluation and Selection of the Strategic Alliance Partner in the Airline Industry”, Journal of Air Transport Management, 52, 55-66.
• Gomes, L.F.A.M., de Mattos Fernandes, J.E. and de Mello, J.C.C.S. (2014). “A Fuzzy Stochastic Approach to the Multicriteria Selection of an Aircraft for Regional Chartering”, Journal of Advanced Transportation, 48(3), 223-237.
• Gramani, M.C.N. (2012). “Efficiency Decomposition Approach: A Cross-Country AIRLINE analysis”, Expert Systems with Applications, 39(5), 5815-5819.
• Hatami-Marbini, A. and Tavana, M. (2011). “An Extension of the ELECTRE I Method for Group Decision-Making Under a Fuzzy Environment”, Omega, 39(4), 373-386.
• Heizer, J., Reder, B. and Muson, C. (2020). “Operations Management: Sustainability and Supply Chain”, United Kingdom, Pearson.
• Hwang, C.L. and Yoon, K. (1981). “Methods for Multiple Attribute Decision Making”, Multiple Attribute Decision Making, Lecture Notes in Economics and Mathematical Systems, vol 186. Springer, Berlin, Heidelberg 58-191.
• Jahanshahloo, G.R., Lotfi, F.H. and Izadikhah, M. (2006). “An Algorithmic Method to Extend TOPSIS for Decision-Making Problems with Interval Data”, Applied Mathematics and Computation, 175(2), 1375-1384.
• Joshi, D. and Kumar, S. (2014). “Intuitionistic Fuzzy Entropy and Distance Measure Based TOPSIS Method for Multi-Criteria Decision Making”, Egyptian Informatics Journal, 15(2), 97-104.
• Junior, F.R.L., Osiro, L. and Carpinetti, L.C.R. (2014). “A Comparison Between Fuzzy AHP and Fuzzy TOPSIS Methods to SUPPLIER selection”, Applied Soft Computing, 21, 194-209.
• Kiraci, K. and Yaşar, M. (2020). “The Determinants of Airline Operational Performance: An Empirical Study on Major World Airlines”. Sosyoekonomi, 28(43), 107-117.
• Krohling, R.A. and Campanharo, V.C. (2011). “Fuzzy TOPSIS for Group Decision Making: A Case Study for Accidents with Oil Spill in the Sea”, Expert Systems with Applications, 38(4), 4190-4197.
• Lee, S., Seo, K. and Sharma, A. (2013). “Corporate Social Responsibility and Firm Performance in the Airline Industry: The Moderating Role of Oil Prices”, Tourism Management, 38, 20-30.
• Lu, W.M., Wang, W.K., Hung, S.W. and Lu, E.T. (2012). “The Effects of Corporate Governance on Airline Performance: Production and Marketing EFFICIENCY Perspectives”, Transportation Research Part E: Logistics and Transportation Review, 48(2), 529-544.
• Mhlanga, O., Steyn, J. and Spencer, J. (2018). “The Airline Industry in South Africa: Drivers of Operational Efficiency and Impacts”, Tourism Review, 73(3), 389-400.
• Narasimhan, R. and Das, A. (2001). “The Impact of Purchasing Integration and Practices on Manufacturing Performance”, Journal of operations Management, 19(5), 593-609.
• Ouellette, P., Petit, P., Tessier-Parent, L.P. and Vigeant, S. (2010). “Introducing Regulation in the Measurement of Efficiency, with an Application to the Canadian Air Carriers Industry”, European Journal of Operational Research, 200(1), 216-226.
• Pinchemel, A., Caetano, M., Rossi, R.M. and Silva, M.A. (2022). “Airline’s Business Performance Indicators and Their Impact on Operational Efficiency”, BBR Brazilian Business Review, 19, 642-665.
• Pineda, P.J.G., Liou, J.J., Hsu, C.C. and Chuang, Y.C. (2018). “An Integrated MCDM Model for Improving Airline Operational and Financial Performance”, Journal of Air Transport Management, 68, 103-117.
• Prascevic, N. and Prascevic, Z. (2017). “Application of Fuzzy AHP for Ranking and Selection of Alternatives in Construction Project Management”, Journal of Civil Engineering and Management, 23(8), 1123-1135.
• Roy, T. and Dutta, R.K. (2019). “Integrated Fuzzy AHP and Fuzzy TOPSIS Methods for Multi-Objective Optimization of Electro Discharge Machining Process”, Soft Computing, 23(13), 5053-5063.
• Samanlioglu, F., Taskaya, Y.E., Gulen, U.C. and Cokcan, O. (2018). “A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection”, International Journal of Fuzzy Systems, 20, 1576-1591.
• Schroeder, R.G., Shah, R. and Xiaosong Peng, D. (2011). “The Cumulative Capability ‘Sand Cone’ Model Revisited: A New Perspective for Manufacturing Strategy”, International Journal of Production Research, 49(16), 4879-4901.
• Schefczyk, M. (1993). “Operational Performance of Airlines: An Extension of Traditional Measurement Paradigms”, Strategic Management Journal, 14(4), 301-317.
• Seufert, J.H., Arjomandi, A. and Dakpo, K.H. (2017). “Evaluating Airline Operational Performance: A Luenberger-Hicks-Moorsteen Productivity Indicator”, Transportation Research Part E: Logistics and Transportation Review, 104, 52-68.
• Sharma, S. and Modgil, S. (2020). “TQM, SCM and Operational Performance: An Empirical Study of Indian Pharmaceutical Industry”, Business Process Management Journal, 26(1), 331-370.
• Shen, F., Xu, J. and Xu, Z. (2016). “An Outranking Sorting Method for Multi-Criteria Group Decision Making Using Intuitionistic Fuzzy Sets”, Information Sciences, 334, 338-353.
• Shen, F., Ma, X., Li, Z., Xu, Z. and Cai, D. (2018). “An Extended Intuitionistic Fuzzy TOPSIS Method Based on a New Distance Measure with an Application to Credit Risk Evaluation”, Information Sciences, 428, 105-119.
• Shojaei, P., Haeri, S.A.S. and Mohammadi, S. (2018). “Airports Evaluation and Ranking Model Using Taguchi Loss Function, Best-Worst Method and VIKOR Technique”, Journal of Air Transport Management, 68, 4-13.
• Singh, A., Joshi, D.K. and Kumar, S. (2019). “A Novel Construction Method of Intuitionistic Fuzzy Set from Fuzzy Set and its Application in Multi-Criteria Decision-Making Problem”, Advanced Computing and Communication Technologies: Proceedings of the 11th ICACCT 2018, Springer, Singapore, 67-75.
• Ye, F. (2010). “An Extended TOPSIS Method with Interval-Valued Intuitionistic Fuzzy Numbers for Virtual Enterprise Partner Selection”, Expert Systems with Applications, 37(10), 7050-7055.
• Yu, M.M., Hsu, S.H., Chang, C.C. and Lee, D.H. (2008). “Productivity Growth of Taiwan’s Major Domestic Airports in the Presence of Aircraft Noise”, Transportation Research Part E: Logistics and Transportation Review, 44(3), 543-554.
• Yu, M.M., Chen, L.H. and Chiang, H. (2017). “The Effects of Alliances and Size on Airlines’ Dynamic Operational Performance”, Transportation Research Part A: Policy and Practice, 106, 197-214.
• Zou, B. and Hansen, M. (2012). “Impact of Operational Performance on Air Carrier Cost Structure: Evidence from US Airlines”, Transportation Research Part E: Logistics and Transportation Review, 48(5), 1032-1048.
• Venkatesh, V.G., Zhang, A., Deakins, E., Luthra, S. and Mangla, S. (2019). “A Fuzzy AHP-TOPSIS Approach to Supply Partner Selection in Continuous Aid Humanitarian Supply Chains”, Annals of Operations Research, 283, 1517-1550.
• Wang, Y.J. (2008). “Applying FMCDM to Evaluate Financial Performance of Domestic Airlines in Taiwan”, Expert Systems with Applications, 34(3), 1837-1845.
• Wang, T.C. and Chang, T.H. (2007). “Application of TOPSIS in Evaluating Initial Training Aircraft under a Fuzzy Environment”, Expert Systems with Applications, 33(4), 870-880.
• Wang, T., Liu, J., Li, J. and Niu, C. (2016). “An Integrating OWA–TOPSIS Framework in Intuitionistic Fuzzy Settings for Multiple Attribute Decision Making”, Computers & Industrial Engineering, 98, 185-194.
• Wang, R.T., 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, 10(5), 353-360.
• Wanke, P. and Barros, C.P. (2016). “Efficiency in Latin American Airlines: A Two-Stage Approach Combining Virtual Frontier Dynamic DEA and Simplex Regression”, Journal of Air Transport Management, 54, 93-103.
• Wanke, P., Barros, C. P. and Chen, Z. (2015). “An Analysis of Asian Airlines Efficiency with Two-Stage TOPSIS and MCMC Generalized Linear Mixed Models”, International Journal of Production Economics, 169, 110-126.
• Wu, W.Y. and Liao, Y.K. (2014). “A Balanced Scorecard Envelopment Approach to Assess Airlines' Performance”, Industrial Management & Data Systems, 114(1), 123-143.
• Wyman, O. (2012). “Guide to Airport Performance Measures”, Airports Council international, ACI.
• Zadeh, L.A. (1965). “Fuzzy Sets”, Information and Control, 8(3), 338-353.
• Zhang, G.P. and Xia, Y. (2013). “Does Quality Still Pay? A Reexamination of the Relationship between Effective Quality Management and Firm Performance”, Production and Operations Management, 22(1), 120-136.