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

Abstract

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. 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. 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

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Journal of Data Analytics and Intelligent Decision-making
Volume 2, Issue 3 - Serial Number 6
September 2026
Pages 185-211

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