Proper process selection during flight schedule disruption using a fuzzy multi-criteria decision-making expert system

Document Type : Research Paper


1 Department of Software Engineering, University of Isfahan, Isfahan, Iran

2 Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran

3 Department of Computer Engineering, Zahedan Branch, Islamic Azad University, Zahedan, Iran


The aviation industry is a complicated, sensitive, and challenging phenomenon.  One of the major issues in the operation of streamlined processes in this industry is the management of proper decisions during the disruption of flight schedules. Such disruptions commonly reduce customer satisfaction and the profitability of the airlines. Since there are multiple reasons for the disruption of the flight schedules along with the different possible decisions, a correct decision is very difficult to make requiring the opinions of the specialist staff. In this research, an expert model using a ``fuzzy multi-criteria decision-making" method is proposed to provide a correct decision during the disruption of the flight schedules. The results show that the most important factors that make disruption of flight schedules are arrival delays and technical failure of the airline fleet. Besides, the most important possible decisions are the announcement of the delay and canceling of the flight. Thanks to utilizing the fuzzy analytical network process, the outcomes of the proposed expert model are in good alignment with the opinions of the specialist staff. The fuzzy analytical network process determines the values of 0.5124 and 0.2621 for the magnitude of the arrival delay and technical defect respectively. This method also determines the values of 0.7042 and 0.2076 for flight delay and flight canceling as the two most important possible decisions.


[1] D. Arias-Aranda, J. Castro, M. Navarro, J. S´anchez, J. A. Zurita, Fuzzy expert system for business management,
Expert Systems With Applications, 37 (2010), 7570-7580.
[2] M. Asfe, M. Zehi, M. Tash, N. Yaghoubi, Ranking different factors influencing flight delay, Management Science
Letters, 4 (2014), 1397-1400.
[3] C. Barnhart, A. Cohn, Airline schedule planning: Accomplishments and opportunities, Manufacturing and Service
Operations Management, 6 (2004), 3-22.
[4] G. Bruno, E. Esposito, A. Genovese, A model for aircraft evaluation to support strategic decisions, Expert Systems
With Applications, 42 (2015), 5580-5590.
[5] N. Cagman, S. Enginoglu, F. Citak, Fuzzy soft set theory and its applications, Iranian Journal of Fuzzy Systems, 8
(2011), 137-147.
[6] D. Chang, Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research, 95
(1996), 649-655.
[7] M. Dunbar, G. Froyland, C. Wu, Robust airline schedule planning: Minimizing propagated delay in an integrated
routing and crewing framework, Transportation Science, 46 (2012), 204-216.
[8] M. Jangizehi, A. Rasouli Kenari, J. Hosseinkhani, Proposing a method to choose the optimal process during an
airline flight plan using a fuzzy network analysis process, The 4th National Conference on Technology in Electrical
and Computer Engineering, (2018), 1-11.
[9] M. Jangizehi, M. Tash, N. Yaghoubi, Choosing optimization process in the event of flight plan interruption with the
aid of network analysis process, International Journal of Engineering, Science and Mathematics, 2 (2013), 46.
[10] A. Jarrah, G. Yu, N. Krishnamurthy, A. Rakshit, A decision support framework for airline flight cancellations and
delays, Transportation Science, 27 (1993), 266-280.
[11] C. Karels, H. McCormick, R. Hodhod, Application of fuzzy expert systems in assessing risk management in the US
army, International Journal of Computer Applications, 113(6) (2015), 10-16.
[12] N. Kaur, N. Rekhi, A. Nayyar, Review of expert systems based on fuzzy logic, International Journal of Advanced
Research in Computer and Communication Engineering, 2 (2013), 1334-1339.
[13] S. Lan, J. Clarke, C. Barnhart, Planning for robust airline operations: Optimizing aircraft routing and flight
departure times to minimize passenger disruptions, Transportation Science, 40 (2006), 15-28.
[14] S. Leo Kumar, Knowledge-based expert system in manufacturing planning: State-of-the-art review, International
Journal of Production Research, 57 (2019), 4766-4790.
[15] Q. Li, R. Jing, Flight delay prediction from spatial and temporal perspective, Expert Systems With Applications,
205 (2022), 117662. DOI:10.1016/j.eswa.2022.117662.
[16] C. Ng, C. Bil, S. Sardina, T. O’bree, Designing an expert system to support aviation occurrence investigations,
Expert Systems With Applications, 207 (2022), 117994.
[17] S. Ohi, A. Kim, Count models to represent the impacts of weather and infrastructure on flight disruptions, Transportation Research Record, 2674 (2020), 510-521.
[18] M. Patel, P. Virparia, D. Patel, Web based fuzzy expert system and its applications-a survey, International Journal
of Applied Information Systems, 1 (2012), 11-15.
[19] S. Pei, Y. He, Z. Fan, B. Zhang, Decision support system for the irregular flight recovery problem, Research In
Transportation Business and Management, 38 (2021), 100501.
[20] A. Serrano-Hernandez, L. Cadarso, J. Faulin, A strategic multistage tactical two-stage stochastic optimization model
for the airline fleet management problem, Transportation Research Procedia, 47 (2020), 473-480.
[21] J. Skorupski, P. Uchro´nski, A fuzzy system to support the configuration of baggage screening devices at an airport,
Expert Systems With Applications, 44 (2016), 114-125.
[22] M. Sohoni, Y. Lee, D. Klabjan, Robust airline scheduling under block-time uncertainty, Transportation Science, 45
(2011), 451-464.
[23] P. Wu, R. Clothier, D. Campbell, R. Walker, Fuzzy multi-objective mission flight planning in unmanned aerial
systems, 2007 IEEE Symposium On Computational Intelligence In Multi-Criteria Decision-Making, (2007), 2-9.
[24] H. Zhang, W. Wu, S. Zhang, F. Witlox, Simulation analysis on flight delay propagation under different network
configurations, IEEE Access, 8 (2020), 103236-103244. [Original source:].