Comparative analysis of airline financial and operational performances: A fuzzy AHP and TOPSIS integrated approach

Bae, K.; Gupta, A.; Mau, R.

Growing Science » Decision Science Letters » Comparative analysis of airline financial and operational performances: A fuzzy AHP and TOPSIS integrated approach

Journals

DSL Volumes

Volume 1 (10)
- Issue 1 (5)
- Issue 2 (5)

Volume 2 (30)
- Issue 1 (5)
- Issue 2 (6)
- Issue 3 (9)
- Issue 4 (10)

Volume 3 (53)
- Issue 1 (15)
- Issue 2 (10)
- Issue 3 (19)
- Issue 4 (9)

Volume 4 (48)
- Issue 1 (10)
- Issue 2 (12)
- Issue 3 (14)
- Issue 4 (12)

Volume 5 (39)
- Issue 1 (12)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (9)

Volume 6 (30)
- Issue 1 (8)
- Issue 2 (6)
- Issue 3 (9)
- Issue 4 (7)

Volume 7 (41)
- Issue 1 (8)
- Issue 2 (8)
- Issue 3 (8)
- Issue 4 (17)

Volume 8 (38)
- Issue 1 (8)
- Issue 2 (6)
- Issue 3 (14)
- Issue 4 (10)

Volume 9 (39)
- Issue 1 (8)
- Issue 2 (9)
- Issue 3 (14)
- Issue 4 (8)

Volume 10 (43)
- Issue 1 (7)
- Issue 2 (8)
- Issue 3 (20)
- Issue 4 (8)

Volume 11 (49)
- Issue 1 (9)
- Issue 2 (9)
- Issue 3 (14)
- Issue 4 (17)

Volume 12 (64)
- Issue 1 (12)
- Issue 2 (24)
- Issue 3 (13)
- Issue 4 (15)

Volume 13 (78)
- Issue 1 (21)
- Issue 2 (18)
- Issue 3 (19)
- Issue 4 (20)

Volume 14 (21)
- Issue 1 (21)

Countries

Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 10 Issue 3 pp. 361-374 , 2021

Comparative analysis of airline financial and operational performances: A fuzzy AHP and TOPSIS integrated approach Pages 361-374 Download PDF

Authors: Ki-Hwan Gabriel Bae, Aman Gupta, Ronald Mau

DOI: 10.5267/j.dsl.2021.2.002

Keywords: Fuzzy AHP, TOPSIS, Multi-Criteria Decision Making (MCDM), Airline, Financial performance

Abstract: Already faced with tight competition and low profit margins, the airline industry is going through major changes in the wake of the current pandemic resulting in travel restrictions and slump demands, prompting airlines to curtail services and investments in every aspect of business. To that end, developing a comprehensive method of improving airline performance measures is crucial. However, this type of problem is complex to solve due to a large number of factors, requiring a systematic approach. It entails taking into account a multitude of conflicting, or sometimes interrelated criteria, hence becoming an inherently multiple criteria decision making problem. This study is aimed to assess the competitiveness of airlines and evaluate their financial and operational performances in relation to such criteria. We test FAHP, TOPSIS, and a hybrid method of combining FAHP and TOPSIS methods. In particular, regarding the hybrid method, FAHP is employed to determine the influential weights of criteria that are utilized in TOPSIS for preference values among alternatives. We demonstrate the applicability of the proposed methods to solving a MCDM problem of airline performance assessments using real data sets. Further, this study focuses on examining the relationship between financial and operational performance criteria, as well as gleaning insights for airlines to build an evaluation system that would aid in understanding their strength and weakness in the performance metrics. The computational experiment results of our hybrid FAHP-TOPSIS model support the efficacy of incorporating fuzzy values concerning influential weight criteria. By judiciously distributing criteria weights that are specific to the airline industry, our proposed model captures preference scores reflective of industry-related and concurrent measures. This modeling framework can help airlines better evaluate the systematic influential relation structure among criteria in critical financial and operational dimensions.

How to cite this paper

 Bae, K., Gupta, A & 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.

Refrences

Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42(24), 9565-9573.
Ayhan, M. B. (2013). A fuzzy AHP approach for supplier selection problem: A case study in a gear motor company. International Journal of Managing Value and Supply Chains, 4, 11-23.
Barrosa, C., & Wanke, P. (2015). An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management, 44, 90-102.
Boutkhoum, O., Hanine, M., & 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.
Brunelli, M. (2014). Introduction to the analytic hierarchy process (1st ed.). Springer.
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247.
Bureau of Transportation Statistics. (2018). https://www.bts.gov/, accessed March 2018.
Chou, S. W., & Chang, Y. C. (2008). The implementation factors that influence the ERP (enterprise resource planning) benefits. Decision Support Systems, 46(1), 149-157.
Ertuğrul, İ., & Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715.
Feng, C. M., & Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142.
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., Fernandes, J., & Soares de Mello, J. (2014). A fuzzy stochastic approach to the multicriteria selection of an aircraft for regional chartering. Journal of Advanced Transportation, 48(3), 223-237.
IATA. (2020). The International Air Transport Association, https://www.iata.org/en/pressroom/pr/2020-04-14-01/, accessed May 2020.
Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: A case study in the Indian automotive industry. Neural Computing and Applications, 29(7), 555-564.
Kannan, D., Jabbour, A. B. L. D. S., & Jabbour, C. J. C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233(2), 432–447.
Lima Jr., F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194-209.
Mergent Online. (2018). http://www.mergentonline.com, accessed March 2018.
Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert Systems with Applications, 41(2), 679-693.
Pineda, P. J. G., Liou, J. J., Hsu, C. C., & 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., & 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., & 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.
Saaty, T. L. (2012). Decision making for leaders: the analytic hierarchy process for decisions in a complex world. (3rd ed.). RWS Publications.
Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A fuzzy AHP–TOPSIS-based group decision-making approach to IT personnel selection. International Journal of Fuzzy Systems, 20(5), 1576-1591.
Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303-318.
Venkatesh, V. G., Zhang, A., Deakins, E., Luthra, S., & Mangla, S. (2019). A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research, 283(1), 1517-1550.
van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems, 11(1-3), 229-241.
Wang, Y. J. (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Systems and Applications, 34(3), 1837-1845.