---

• http://dx.doi.org/10.17093/alphanumeric.369758

• Research Article

• Turkish

• OR

---

A Hybrid Multi-Criteria Decision Making Approach for New Destination Selection in Aviation Industry

---

Tuncay Özcan, Ph.D.

Abstract

New destination selection is one of the most important decision making process for airline companies. In this study, a multi-criteria decision making approach for the new destination selection problem is proposed by integrating fuzzy analytical hierarchy process and grey relational analysis methodologies. Fuzzy analytical hierarchy process is used to determine the weight of each criteria and grey relational analysis is used to rank the performance of the alternative destinations in the proposed approach. The effectiveness and applicability of the proposed approach is illustrated with a case study with data taken from an airline company in Turkey.

Keywords: Aviation Industry, Destination Selection, Fuzzy Analytical Hierarchy Process, Grey Relational Analysis

Jel Classification: C44

Havacılık Endüstrisinde Yeni Destinasyon Seçimi İçin Hibrit Bir Çok Kriterli Karar Verme Yaklaşımı

---

Öz

Yeni destinasyon seçimi, havayolu şirketleri için en önemli karar verme süreçlerinden biridir. Bu çalışmada; bulanık analitik hiyerarşi prosesi ve gri ilişki analizi metodolojileri bütünleştirilerek yeni destinasyon seçimi problemi için bir çok kriterli karar verme yaklaşımı önerilmiştir. Önerilen yaklaşımda, herbir kriterin ağırlığını belirlemede bulanık analitik hiyerarşi prosesi ve alternatif destinasyonların performansını sıralamada gri ilişki analizi kullanılmıştır. Önerilen yaklaşımın etkinliği ve uygulanabilirliği Türkiye’deki bir havayolu şirketinden alınan verilerle gerçekleştirilen bir vaka çalışması ile sunulmuştur.

Anahtar Kelimeler: Bulanık Analitik Hiyerarşi Prosesi, Destinasyon Seçimi, Gri İlişkisel Analiz, Havacılık Endüstrisi

---

Suggested citation

Özcan, T. (2018). Havacılık Endüstrisinde Yeni Destinasyon Seçimi İçin Hibrit Bir Çok Kriterli Karar Verme Yaklaşımı. Alphanumeric Journal, 6(1), 1-12. http://dx.doi.org/10.17093/alphanumeric.369758

bibtex

References

• Barnhart, C., Kniker, T. S., & Lohatepanont, M. (2002). Itinerary-based airline fleet assignment. Transportation Science, 36(2), 199-217.
• Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
• Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
• Chang, Y. H., & Yeh, C. H. (2002). A survey analysis of service quality for domestic airlines. European journal of operational research, 139(1), 166-177.
• Chien‐Chang, C. (2012). Evaluating the quality of airport service using the fuzzy multi‐criteria decision‐making method: a case study of Taiwanese airports. Expert Systems, 29(3), 246-260.
• Deng, J.-L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294.
• Ding, H., Lim, A., Rodrigues, B., & Zhu, Y. (2005). The over-constrained airport gate assignment problem. Computers & Operations Research, 32(7), 1867-1880.
• 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.
• Ghobbar, A. A., & Friend, C. H. (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Computers & Operations Research, 30(14), 2097-2114.
• Grosche, T., Rothlauf, F., & Heinzl, A. (2007). Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13(4), 175-183.
• Hsieh, T. Y., Lu, S. T., & Tzeng, G. H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International journal of project management, 22(7), 573-584.
• Hsu, C. C., & Liou, J. J. (2013). An outsourcing provider decision model for the airline industry. Journal of Air Transport Management, 28, 40-46.
• Janic, M., & Reggiani, A. (2002). An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. European journal of transport and infrastructure research EJTIR, 2(2), 114-141.
• Kahraman, C., Süder, A., & Kaya, İ. (2014). Fuzzy multicriteria evaluation of health research investments. Technological and Economic Development of Economy, 20(2), 210-226.
• Lee, H. S., & Chou, M. T. (2006). A fuzzy multiple criteria decision making model for airline competitiveness evaluation. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (s. 902-909). Berlin: Springer.
• Lee, L. H., Lee, C. U., & Tan, Y. P. (2007). A multi-objective genetic algorithm for robust flight scheduling using simulation. European Journal of Operational Research, 177(3), 1948-1968.
• Liou, J. J. (2012). Developing an integrated model for the selection of strategic alliance partners in the airline industry. Knowledge-Based Systems, 28, 59-67.
• Lohatepanont, M., & Barnhart, C. (2004). Airline schedule planning: Integrated models and algorithms for schedule design and fleet assignment. Transportation Science, 38(1), 19-32.
• Nam, K., & Schaefer, T. (tarih yok). Forecasting international airline passenger traffic using neural networks. Logistics and Transportation Review, 31(3), 239.
• Regattieri, A., Gamberi, M., Gamberini, R., & Manzini, R. (2005). Managing lumpy demand for aircraft spare parts. Journal of Air Transport Management, 11(6), 426-431.
• Rexing, B., Barnhart, C., Kniker, T., & Jarrah, A. (2000). Airline fleet assignment with time windows. Transportation Science, 34(1), 1-20.
• Rezaei, J., Fahim, P. B., & Tavasszy, L. (2014). Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications, 41(18), 8165-8179.
• Suryani, E., Chou, S. Y., & Chen, C. H. (2010). Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems with Applications, 37(3), 2324-2339.
• Van Laarhoven, P. J., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy sets and Systems, 11(1-3), 229-241.
• Van Ryzin, G., & McGill, J. (2000). Revenue management without forecasting or optimization: An adaptive algorithm for determining airline seat protection levels. Management Science, 46(6), 760-775.
• Wanke, P., Barros, C. P., & 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, H. H. (2002). A comparative study of using grey relational analysis in multiple attribute decision making problems. Quality Engineering, 2, 209-217.
• Xiong, Y. (2007). Grey relational evaluation of financial situation of listed company. Journal of Modern Accounting and Auditing, 3(2), 41-44.
• Xu, J., & Bailey, G. (2001). The airport gate assignment problem: mathematical model and a tabu search algorithm. In System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on, (s. 10).
• Zhang, D., & Cooper, W. L. (2005). Revenue management for parallel flights with customer-choice behavior. Operations Research, 53(3), 415-431.

2018.06.01.OR.01

alphanumeric journal

Volume 6, Issue 1, 2018

Pages 1-12

Received: Oct. 11, 2017

Accepted: Dec. 25, 2017

Published: Jan. 1, 2018

Full Text [584.6 KB]

2018 Özcan, T.

This is an Open Access article, licensed under Creative Commons Attribution-NonCommercial 4.0 International License.

scan QR code to access this article from your mobile device

Contact Us

Faculty of Transportation and Logistics, Istanbul University
Beyazit Campus 34452 Fatih/Istanbul/TURKEY

Bahadır Fatih Yıldırım, Ph.D.
editor@alphanumericjournal.com
+ 90 (212) 440 00 00 - 13219

alphanumeric journal

alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.