Robust Mathematical Modeling for Air Multiple Logit Design in Flight Route Selection

Document Type : Original Article

Authors

1 Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran

2 Department of Industrial Engineering, Sar.C., Islamic Azad University, Sari, Iran

10.22091/jdaid.2026.16639.1084

Abstract

Goal and Background: Multiple Logit design is recognized as a pivotal tool for analyzing and selecting flight routes. Given the inherent complexities of this industry, including sudden fluctuations in passenger demand, technical and economic constraints, and stringent safety regulations and standards, the application of robust mathematical modeling for multiple logit design becomes imperative. models must possess the capability to manage uncertainties and analyze various scenarios to facilitate optimal route selection decisions.
Methodology: study focuses on designing an air multiple logit model using robust mathematical modeling for flight route selection. The proposed model effectively contributes to reducing costs and improving the efficiency of flight routes by incorporating various factors, including aircraft departure costs, flight crew wages, flight duration between two cities, and other parameters such as passenger capacity per route. The designed model was formulated and solved using GAMS software. The global optimal solutions for the three objective functions of the proposed model were calculated.
Results: Based on the obtained results, the first objective function is 182.8240 monetary units, the second is 540.6540 units, and the third is 200.369 monetary units. This model offers the necessary flexibility for optimal flight route management by accounting for uncertainties and market dynamics. The results indicate that implementing this robust model leads to an increase in aircraft departure costs with an increase in samples, while the total objective function value remains stable. Furthermore, flight crew wages remained constant with increasing samples, and this did not result in an increase in the total cost function value

Keywords

Main Subjects

References

Abolghasemian, M., Bigdeli, H., & Shamami, N. (2024). Locating Routing Problem (LRP) of distribution of priority support items to ground forces in war conditions. Engineering Management and Soft Computing, 10(1), 262-292. https://doi.org/10.22091/jemsc.2024.11320.1206
Akbaba, A. (2023). Determination of new flight route with DEA method in airline operations. PressAcademia Procedia, 17(1), 236-237.
Babagolzadeh, M., Zhang, Y., Abbasi, B., Shrestha, A., & Zhang, A. (2022). Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem. Transport Policy, 128, 38-51. https://doi.org/10.1016/j.tranpol.2022.08.014
Bergantino, A. S., Capurso, M., & Hess, S. (2020). Modelling regional accessibility to airports using discrete choice models: An application to a system of regional airports. Transportation Research Part A: Policy and Practice, 132, 855-871. https://doi.org/10.1016/j.tra.2019.12.012
Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American statistical association, 39(227), 357-365. https://doi.org/10.1080/01621459.1944.10500699
Cardell, N. S., & Dunbar, F. C. (1980). Measuring the societal impacts of automobile downsizing. Transportation Research Part A: General, 14(5-6), 423-434. https://doi.org/10.1016/0191-2607(80)90060-6
Jiang, Y., & Zhang, A. (2023). Large long-haul aircraft entry, survival, and exit in China's air transport market: The Airbus 380 and Boeing 747-8I. Transport Economics and Management, 1, 168-179. https://doi.org/10.1016/j.team.2023.10.002
Jiang, Y., Tran, T. H., & Williams, L. (2023). Machine learning and mixed reality for smart aviation: Applications and challenges. Journal of Air Transport Management, 111, 102437. https://doi.org/10.1016/j.jairtraman.2023.102437
Jiang, Y., Wang, C., Wang, L., Liu, S., & Zhang, A. (2026). Market reactions to new aircraft deployment in China’s domestic Aviation: A panel vector autoregression analysis of C919. Transportation Research Part A: Policy and Practice, 205, 104883. https://doi.org/10.1016/j.tra.2026.104883
Khaleghi, A., & Eydi, A. (2023). Multi-period hub location problem considering polynomial time-dependent demand. Computers & Operations Research, 159, 106357. https://doi.org/10.1016/j.cor.2023.106357
Khan, W. A., Chung, S. H., Eltoukhy, A. E., & Khurshid, F. (2024). A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis. Journal of Air Transport Management, 114, 102488. https://doi.org/10.1016/j.jairtraman.2023.102488
Kim, S. H. (2020). Choice model-based analysis of consumer preference for drone delivery service. Journal of Air Transport Management, 84, 101785. https://doi.org/10.1016/j.jairtraman.2020.101785
Lu, J., Meng, Y., Timmermans, H., & Zhang, A. (2021). Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods. Transportation Research Part A: Policy and Practice, 147, 230-250. https://doi.org/10.1016/j.tra.2021.03.006
McFadden, D. (1974). The measurement of urban travel demand. Journal of Public Economics, 3(4), 303-328. https://doi.org/10.1016/0047-2727(74)90003-6
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264-281. https://doi.org/10.1287/opre.43.2.264 
Pamucar, D., Deveci, M., Canıtez, F., & Lukovac, V. (2020). Selecting an airport ground access mode using novel fuzzy LBWA-WASPAS-H decision making model. Engineering Applications of Artificial Intelligence, 93, 103703. https://doi.org/10.1016/j.engappai.2020.103703
Şi̇mşek, D., & Aktürk, M. S. (2022). Resilient airline scheduling to minimize delay risks. Transportation Research Part C: Emerging Technologies, 141, 103734. https://doi.org/10.1016/j.trc.2022.103734
Sun, J., & Wang, R. (2023). Multi-objective optimization of a sustainable two echelon vehicle routing problem with simultaneous pickup and delivery in construction projects. Journal of Engineering Research, 13(1), 39-46. https://doi.org/10.1016/j.jer.2023.10.033
Sun, X., Zheng, C., Wandelt, S., & Zhang, A. (2024). Airline competition: A comprehensive review of recent research. Journal of the Air Transport Research Society, 2, 100013. https://doi.org/10.1016/j.jatrs.2024.100013
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, 102302. https://doi.org/10.1016/j.jairtraman.2022.102302
Teshome, M. B., Podrecca, M., & Orzes, G. (2026). Mapping innovation pathways and future potential of electric cars: Insights from patent data analysis. Transport Economics and Management. https://doi.org/10.1016/j.team.2026.05.001
Unal, Y. Z., Sevkli, M., Uysal, O., & Turkyilmaz, A. (2021). A new approach to fleet assignment and aircraft routing problems. Transportation Research Procedia, 59, 67-75. https://doi.org/10.1016/j.trpro.2021.11.098
Wandelt, S., Wang, S., Chen, X., Zheng, C., Chang, S., & Sun, X. (2025) Network structures in air transportation: A comprehensive review of applications and challenges. Journal of Air Transport Management, 126, 102794. https://doi.org/10.1016/j.jairtraman.2025.102794
Wong, C. W., Cheung, T. K. Y., & Zhang, A. (2023) A connectivity-based methodology for new air route identification. Transportation Research Part A: Policy and Practice, 173, 103715. https://doi.org/10.1016/j.tra.2023.103715
Esmaeili, F., & Marjani, M. R. (2026). The integrated problem of locating, allocating, and routing transportation vehicles in the blood product supply chain during crisis conditions. Journal of Engineering Management and Soft Computing, 12(3), 1-19. doi: 10.22091/jemsc.2026.12252.1254
Mousavi, S. T., Faezy Razi, F., & Danaei, A. (2026). Co-EvoDT: Design of a Co-evolving Digital Twin Framework for Media Digital Transformation in the Metaverse through Integration of Multi-Agent Reinforcement Learning and LSTM-based Sequential Forecasting. Journal of Engineering Management and Soft Computing, 12(3), 39-61. doi: 10.22091/jemsc.2026.14030.1306
Akhshabi, M., & Arab, J. (2026). Designing a multi-objective mathematical model for optimizing a shop floor flow production system, considering the number of human activities using the Gray Wolf metaheuristic algorithm. Journal of Engineering Management and Soft Computing, 12(2), 1-18. doi: 10.22091/jemsc.2026.12210.1253
Molaei, M., Moradi-Kamali, H., & Entezari-Maleki, R. (2026). Forecasting Stock Market Volatility: A Wavelet-Enhanced Hybrid GARCH-Deep Learning Approach. Journal of Engineering Management and Soft Computing, 12(2), 56-74. doi: 10.22091/jemsc.2026.13545.1291
Banaei, S. M., Roozbahani, M. H., & Sohofi, A. (2026). Bridging the Gap in Fetal Heart Rate Monitoring: Introduction and Evaluation of an Acoustic-Based Smart Belt for Real-Time, Passive, Mother-Centered Monitoring with Extensive Multicenter Validation. Journal of Engineering Management and Soft Computing, 12(1), 1-13. doi: 10.22091/jemsc.2026.15145.1340
Sayyah, A. (2026). Identification and Evaluation of Deep Learning-Based Defense Strategies to Counter DDoS Attacks in Cloud Computing Environments. Journal of Engineering Management and Soft Computing, 12(1), 36-48. doi: 10.22091/jemsc.2026.14316.1317
Yousefi Nejad, M., Farajian, K., & Jaleb, H. (2025). Analysis of factors affecting the length of stay of patients using clustering and association rules (Case study: Amir al-Momenin Hospital, Maragheh). Journal of Engineering Management and Soft Computing, 11(2), 1-20. doi: 10.22091/jemsc.2025.11174.1195
Hosseini, S. J., Mohaghar, A., Mokhtari, G., & Maleki, M. H. (2025). Modeling the Combined Problem of Determining the Lot Sizing and Cutting Stock of Raw Materials and Solving With Deep Reinforcement Learning and Exact Method. Journal of Engineering Management and Soft Computing, 11(2), 326-347. doi: 10.22091/jemsc.2025.14699.1328
Abdolhossein Zadeh, S., Zandieh, M., & Alam-Tabriz, A. (2025). E-Healthcare Improvement through the Design of a Cloud-Based Blood Supply Chain: A System Dynamics Approach. Journal of Engineering Management and Soft Computing, 11(1), 47-1. doi: 10.22091/jemsc.2025.11210.1201
Mahdian, M., & Matinkhah, S. M. (2025). Enhancing Service Recommendations in the Social Internet of Things: An Adaptive Collaborative Filtering Approach Using Friendship-Based Similarity. Journal of Engineering Management and Soft Computing, 11(1), 114-82. doi: 10.22091/jemsc.2025.11431.1210
Jafari, M., & Akbari, A. H. (2025). Development of a Deep Reinforcement Learning Algorithm in a Dynamic Cellular Manufacturing System Considering Order Rejection, Case Study: Stone Paper Factory. Journal of Engineering Management and Soft Computing, 10(2), 204-222. doi: 10.22091/jemsc.2025.11853.1230
Izadi, E., Nikbakht, M., Feylizadeh, M., & Shahin, A. (2025). Ranking of criteria affecting humanitarian supply chain services based on blockchain platforms using multi-criteria decision-making methods. Journal of Engineering Management and Soft Computing, 10(2), 143-160. doi: 10.22091/jemsc.2025.11552.1215
Dehghan, R., Naderan, M., & Alavi, S. E. (2024). Combining Convolutional Neural Network (CNN) and Grad-CAM for Parkinson’s Disease Prediction and Visual Explanation. Journal of Engineering Management and Soft Computing, 10(1), 1-13. doi: 10.22091/jemsc.2024.10828.1180
Abolghasemian, M., Bigdeli, H., & Shamami, N. (2024). Locating Routing Problem (LRP) of distribution of priority support items to ground forces in war conditions. Journal of Engineering Management and Soft Computing, 10(1), 262-292. doi: 10.22091/jemsc.2024.11320.1206
Journal of Data Analytics and Intelligent Decision-making
Volume 2, Issue 3 - Serial Number 6
September 2026
Pages 185-211

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