Airlines’ operational resilience assessment: integrating BWM, TODIM, and rough DOMBI methods
DOI: https://doi.org/10.3846/tede.2026.28210Abstract
The aviation sector is among the most vulnerable industries to unforeseen disruptions, including natural disasters, pandemics, and international conflicts. Airlines are increasingly recognizing the critical importance of business resilience in managing these disruptions. This study develops a comprehensive framework that utilizes Rough Dombi and Z-number fuzzy methodologies, combined with multi-criteria decision-making (MCDM) approaches, to evaluate criteria and solutions influencing aviation industry resilience in Taiwan through a case study of four airlines. First, an evaluation framework for airline resilience is established through a literature review and expert interviews. The Best-Worst Method (BWM) determines the weights of evaluation indicators, while the TODIM (Tomada de Decisão Interativa Multicritério) method identifies gaps between each company’s performance and ideal values. Results reveal that “Management expertise sensitive to environmental changes and able to enhance abilities beyond their own business” is the most influential factor, and Airline A2 serves as a benchmark for others. Sensitivity analysis with varying criterion weights and comparative analysis with alternative MCDM techniques validate the robustness and reliability of the findings.
First published online 29 September 2026
Keywords:
resilience, rough Z-numbers, DOMBI, BWM, TODIMHow to Cite
Share
Funding data
-
National Science and Technology Council
Grant numbers: 113-2625-M-027-008
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aczél, J., & Alsina, C. (1982). Characterizations of some classes of quasilinear functions with applications to triangular norms and to synthesizing judgments. Aequationes Mathematicae, 25(1), 313–315. https://doi.org/10.1007/BF02189626
Albuquerque, R. W., Costa, M. O., Ferreira, M. E., Carrero, G. C., & Grohmann, C. H. (2020). Remotely piloted aircraft imagery for automatic tree counting in forest restoration areas: A case study in the Amazon. The Journal of Unmanned Vehicle Systems, 8(3), 207–223. https://doi.org/10.1139/juvs-2019-0024
Allen, J. H., & Davis, N. (2010). Measuring operational resilience using the CERT® Resilience Management Model (Technical Note 84). Software Engineering Institute, Carnegie Mellon University.
Ayyildiz, E., Yildiz, A., Taskin, A., & Ozkan, C. (2023). An interval valued Pythagorean fuzzy AHP integrated quality function deployment methodology for hazelnut production in Turkey. Expert Systems with Applications, 231, Article 120708. https://doi.org/10.1016/j.eswa.2023.120708
Bahrami, S., & Rastegar, M. (2022). Security-based critical power distribution feeder identification: Application of fuzzy BWM-VIKOR and SECA. International Journal of Electrical Power & Energy Systems, 134, Article 107395. https://doi.org/10.1016/j.ijepes.2021.107395
Ball, M., Barnhart, C., Nemhauser, G., & Odoni, A. (2007). Air transportation: Irregular operations and control. In Handbooks in operations research and management science (Vol. 14, pp. 1–67). Elsevier. https://doi.org/10.1016/S0927-0507(06)14001-3
Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163, Article 120447. https://doi.org/10.1016/j.techfore.2020.120447
Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: The concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375–5393. https://doi.org/10.1080/00207543.2011.563826
Bülbül, K. G. (2022). A necessity for sustainability: Operational resilience through disruption management in airlines. In K. Kiracı & K. T. Çalıyurt (Eds.), Accounting, finance, sustainability, governance & fraud: theory and application: Vol. 1. Corporate governance, sustainability, and information systems in the aviation sector (pp. 11–35). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9276-5_2
Cardillo, A., Zanin, M., Gómez-Gardeñes, J., Romance, M., García del Amo, A. J., & Boccaletti, S. (2013). Modeling the multi-layer nature of the European air transport network: Resilience and passengers rescheduling under random failures. The European Physical Journal Special Topics, 215(1), 23–33. https://doi.org/10.1140/epjst/e2013-01712-8
Carpenter, S., Walker, B., Anderies, J. M., & Abel, N. (2001). From metaphor to measurement: Resilience of what to what? Ecosystems, 4(8), 765–781. https://doi.org/10.1007/s10021-001-0045-9
Chen, N., & Ye, J. (2017). Some single-valued neutrosophic Dombi weighted aggregation operators and their application in multiple attribute decision-making. Symmetry, 9(6), Article 82. https://doi.org/10.3390/sym9060082
Darko, A., Chan, A. P. C., Ameyaw, E. E., Owusu, E. K., Pärn, E., & Edwards, D. J. (2019). Review of application of analytic hierarchy process (AHP) in construction. International Journal of Construction Management, 19(5), 436–452. https://doi.org/10.1080/15623599.2018.1452098
Demers, E., Hendrikse, J., Joos, P., & Lev, B. (2021). ESG did not immunize stocks during the COVID-19 crisis, but investments in intangible assets did. Journal of Business Finance & Accounting, 48(3–4), 433–462. https://doi.org/10.1111/jbfa.12523
Ding, Q., Wang, Y.-M., & Goh, M. (2021). An extended TODIM approach for group emergency decision making based on bidirectional projection with hesitant triangular fuzzy sets. Computers & Industrial Engineering, 151, Article 106959. https://doi.org/10.1016/j.cie.2020.106959
Dombi, J. (1982). A general class of fuzzy operators, the De Morgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets and Systems, 8(2), 149–163. https://doi.org/10.1016/0165-0114(82)90005-7
Ecer, F., Tanrıverdi, G., Yaşar, M., & Görçün, Ö. F. (2025). Sustainable aviation fuel supplier evaluation for airlines through LOPCOW and MARCOS approaches with interval-valued fuzzy neutrosophic information. Journal of Air Transport Management, 123, Article 102705. https://doi.org/10.1016/j.jairtraman.2024.102705
Essuman, D., Boso, N., & Annan, J. (2020). Operational resilience, disruption, and efficiency: Conceptual and empirical analyses. International Journal of Production Economics, 229, Article 107762. https://doi.org/10.1016/j.ijpe.2020.107762
Fan, F., Weng, Z., & Tian, J. (2024). Impact of government support policies on regional economic resilience under the COVID-19 outbreak. Technological and Economic Development of Economy, 30(1), 74–106. https://doi.org/10.3846/tede.2024.20397
Frank, M. J. (1979). On the simultaneous associativity of F(x, y) and x + y − F(x, y). Aequationes Mathematicae, 19(1), 194–226. https://doi.org/10.1007/BF02189866
Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and fuzzy TOPSIS. Journal of Environmental Management, 226, 201–216. https://doi.org/10.1016/j.jenvman.2018.08.005
Holling, C. S. (1973). Resilience and stability of ecological systems. International Institute for Applied Systems Analysis. https://doi.org/10.1146/annurev.es.04.110173.000245
Hsu, C. H., Li, M. G., Zhang, T. Y., Chang, A. Y., Shangguan, S. Z., & Liu, W. L. (2022). Deploying big data enablers to strengthen supply chain resilience to mitigate sustainable risks based on integrated HOQ-MCDM framework. Mathematics, 10(8), Article 1233. https://doi.org/10.3390/math10081233
Jiang, F., Kong, D., Lu, Z., Ma, Y., & Yi, Y. (2023). Geographic dispersion and corporate resilience during the COVID-19 pandemic. International Review of Financial Analysis, 88, Article 102684. https://doi.org/10.1016/j.irfa.2023.102684
Jimenez Serrano, F. J., & Kazda, A. (2017). Airline disruption management: Yesterday, today and tomorrow. Transportation Research Procedia, 28, 3–10. https://doi.org/10.1016/j.trpro.2017.12.162
Kang, B., Wei, D., Li, Y., & Deng, Y. (2012). Decision making using Z-numbers under uncertain environment. Journal of Computer Information Systems, 8(7), 2807–2814.
Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12
Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2017). Stochastic EDAS method for multi-criteria decision-making with normally distributed data. Journal of Intelligent & Fuzzy Systems, 33(3), 1627–1638. https://doi.org/10.3233/JIFS-17184
Kou, G., Chao, X., Peng, Y., & Wang, F. (2022). Network resilience in the financial sectors: Advances, key elements, applications, and challenges for financial stability regulation. Technological and Economic Development of Economy, 28(3), 531–558. https://doi.org/10.3846/tede.2022.16500
Lee, K.-C., Tsai, W.-H., Yang, C.-H., & Lin, Y.-Z. (2018). An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations. Journal of Air Transport Management, 68, 76–85. https://doi.org/10.1016/j.jairtraman.2017.06.011
Li, M.-Y., & Cao, P.-P. (2019). Extended TODIM method for multi-attribute risk decision making problems in emergency response. Computers & Industrial Engineering, 135, 1286–1293. https://doi.org/10.1016/j.cie.2018.06.027
López, C., Ishizaka, A., Gul, M., Yücesan, M., & Valencia, D. (2022). A calibrated fuzzy Best–Worst Method to reinforce supply chain resilience during the COVID-19 pandemic. International Journal of Production Research, 60(19), 1968–1991. https://doi.org/10.1080/01605682.2022.2122739
Markatos, D. N., & Pantelakis, S. G. (2023). Implementation of a holistic MCDM-based approach to assess and compare aircraft, under the prism of sustainable aviation. Aerospace, 10(3), Article 240. https://doi.org/10.3390/aerospace10030240
Markatos, D. N., Malefaki, S., & Pantelakis, S. G. (2023). Sensitivity analysis of a hybrid MCDM model for sustainability assessment—An example from the aviation industry. Aerospace, 10(4), Article 385. https://doi.org/10.3390/aerospace10040385
Mateusz, P., Danuta, M., Małgorzata, L., Mariusz, B., & Kesra, N. (2018). TOPSIS and VIKOR methods in study of sustainable development in the EU countries. Procedia Computer Science, 126, 1683–1692. https://doi.org/10.1016/j.procs.2018.08.109
Migdadi, Y. K. A. A. (2022). Airline effective operations strategy during COVID-19 pandemic: Across regional worldwide survey. Review of International Business and Strategy, 32(1), 10–38. https://doi.org/10.1108/RIBS-11-2020-0146
Minciu, M., Veith, C., Dobrea, R. C., & Ionescu, V.-C. (2025). Adaptive strategies and sustainable investments: Navigating organizations through a VUCA environment in and after COVID-19. Technological and Economic Development of Economy, 31(2), 639–662. https://doi.org/10.3846/tede.2024.22058
Mohammed, A., Yazdani, M., Oukil, A., & Santibanez Gonzalez, E. D. R. (2021). A hybrid MCDM approach towards resilient sourcing. Sustainability, 13(5), Article 2695. https://doi.org/10.3390/su13052695
Pamučar, D., & Božanić, D. (2019). Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model. Operational Research in Engineering Sciences: Theory and Applications, 2(2), 55–71. https://doi.org/10.31181/oresta1902039p
Pamučar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM). Symmetry, 10(9), Article 393. https://doi.org/10.3390/sym10090393
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11(5), 341–356. https://doi.org/10.1007/BF01001956
Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124–143. https://doi.org/10.1108/09574090910954873
Rezaei, J. (2024). A state-of-the-art review of the BWM method and future research agenda. Journal of Cleaner Production, 460, Article 142039. https://doi.org/10.1016/j.jclepro.2024.142039
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Sabillon, R., & Bermejo Higuera, J. R. (2023). The importance of cybersecurity awareness training in the aviation industry for early detection of cyberthreats and vulnerabilities. In International Conference on Human-Computer Interaction (pp. 461–479). https://doi.org/10.1007/978-3-031-48057-7_29
Serfontein, E., & Govender, K. K. (2021). The relationship between resilience and organisational control systems in the South African aviation industry. Journal of Transport and Supply Chain Management, 15, Article a598. https://doi.org/10.4102/jtscm.v15i0.598
Shi, T., Qiao, Y., Zhou, Q., & Zhang, J. (2022). The regional differences and random convergence of urban resilience in China. Technological and Economic Development of Economy, 28(3), 979–1002. https://doi.org/10.3846/tede.2022.16721
Sobieralski, J. B. (2020). COVID-19 and airline employment: Insights from historical uncertainty shocks to the industry. Transportation Research Interdisciplinary Perspectives, 5, Article 100123. https://doi.org/10.1016/j.trip.2020.100123
Stolker, R., Karydas, D., & Rouvroye, J. (2008). A comprehensive approach to assess operational resilience. In Proceedings of the Third Resilience Engineering Symposium (pp. 247–253).
Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066
Tanrıverdi, G., Ecer, F., & Durak, M. Ş. (2022). Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers’ perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology. Journal of Air Transport Management, 105, Article 102302. https://doi.org/10.1016/j.jairtraman.2022.102302
Tekic, Z., & Koroteev, D. (2019). From disruptively digital to proudly analog: A holistic typology of digital transformation strategies. Business Horizons, 62(6), 683–693. https://doi.org/10.1016/j.bushor.2019.07.002
Tian, C., Peng, J.-J., Zhang, W.-Y., Zhang, S., & Wang, J.-Q. (2020). Tourism environmental impact assessment based on improved AHP and picture fuzzy PROMETHEE II methods. Technological and Economic Development of Economy, 30(4), 733–752. https://doi.org/10.3846/tede.2019.11413
Tian, X., Niu, M., Zhang, W., & Li, L. (2021). A novel TODIM based on prospect theory to select green supplier with q-rung orthopair fuzzy set. Technological and Economic Development of Economy, 27(2), 284–310. https://doi.org/10.3846/tede.2020.12736
Wan, C., Yang, Z., Zhang, D., Yan, X., & Fan, S. (2018). Resilience in transportation systems: A systematic review and future directions. Transport Reviews, 38(4), 479–498. https://doi.org/10.1080/01441647.2017.1383532
Wang, H., Jiao, S., & Ma, C. (2024). The impact of ESG responsibility performance on corporate resilience. International Review of Economics & Finance, 93, 1115–1129. https://doi.org/10.1016/j.iref.2024.05.033
Xu, M., Xu, Q., Wei, S., & Cong, L. (2024). The role of organizational resilience in social, economic, environmental and human sustainability: Evidence from Chinese FMCG sector. Technological and Economic Development of Economy, 30(3), 733–752. https://doi.org/10.3846/tede.2024.20476
Yaran Ögel, İ., Aygün Özgöz, A., & Ecer, F. (2023). Prioritizing causes and drivers of retail food waste through a fuzzy Dombi-Bonferroni operators-based best–worst approach: An emerging economy perspective. Environmental Science and Pollution Research, 30(2), 4899–4916. https://doi.org/10.1007/s11356-022-22553-4
Yazdi, M., Khan, F., Abbassi, R., & Rusli, R. (2020). Improved DEMATEL methodology for effective safety management decision-making. Safety Science, 127, Article 104705. https://doi.org/10.1016/j.ssci.2020.104705
Yu, X., Zhang, S., Liao, X., & Qi, X. (2018). ELECTRE methods in prioritized MCDM environment. Information Sciences, 424, 301–316. https://doi.org/10.1016/j.ins.2017.09.061
Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences, 181(14), 2923–2932. https://doi.org/10.1016/j.ins.2011.02.022
Zarei, E., Ramavandi, B., Darabi, A. H., & Omidvar, M. (2021). A framework for resilience assessment in process systems using a fuzzy hybrid MCDM model. Journal of Loss Prevention in the Process Industries, 69, Article 104375. https://doi.org/10.1016/j.jlp.2020.104375
Zeng, H., Khokhar, M., Islam, T., & Sharma, A. (2023). A model for green-resilient supplier selection: Fuzzy best–worst multi-criteria decision-making method and its applications. Environmental Science and Pollution Research, 30, 54035–54058. https://doi.org/10.1007/s11356-023-25857-8
Zhang, D., Lyu, B., Wu, J., Li, W., & Zhang, K. (2023). Effect of boxers’ social support on mental fatigue: Chain mediating effects of coach leadership behaviors and psychological resilience. Work, 76(4), 1465–1479. https://doi.org/10.3233/WOR-220478
Zhang, Y., Yu, J., Fang, Y., Tsai, S.-B., & Chen, Q. (2020). Green transport efficiency and factor mismatch of airlines under environmental constraints. International Journal of Environment and Technology Management, 23(2–4), 293–305. https://doi.org/10.1504/IJETM.2020.112955
Zhao, M., Gao, H., Wei, G., Wei, C., & Guo, Y. (2022). Model for network security service provider selection with probabilistic uncertain linguistic TODIM method based on prospect theory. Technological and Economic Development of Economy, 28(3), 638–654. https://doi.org/10.3846/tede.2022.16483
Zhu, G.-N. (2022). Design concept evaluation considering information reliability, uncertainty, and subjectivity: An integrated rough-Z-number-enhanced MCGDM methodology. Advanced Engineering Informatics, 54, Article 101796. https://doi.org/10.1016/j.aei.2022.101796
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Share
Funding data
-
National Science and Technology Council
Grant numbers: 113-2625-M-027-008