Air traffic flow management of incoming flights with MOGWO and NSGAII approach (Case study: Mashhad International Airport)

Subject Areas : Industrial Management

manezhe Teimoori ¹ (Ph.D. candidate, department of management, Tabriz branch, Islamic Azad University)
Houshang Taghi Zadeh ² (Associate Professor,, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Jafar pourmahmoud ³ (Shahid Madani University of Azerbaijan, Department of Applied Mathematics, Tabriz, Iran)
Morteza Honarmand Azimi ⁴ (Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran)

Received: 2021-07-22 Accepted : 2021-10-30 Published : 2022-02-01

Keywords: Flow Management, NSGAII, MOGWO, Air Traffic,

Abstract :

Landing earlier or later than the specified time will cost each flight, and existing techniques do not measure time factors, so the cost of delay penalties is very high, while there are many techniques. In order to reduce this fine, in this article, the ALP problem is investigated and then, with the help of an optimization function, a model to increase efficiency is presented. In general, the problem of sequencing tasks to determine the order of their execution on machines to reduce ( Or increase) is the criterion in question in optimizing the order of entry operations, both in regulations and in scientific journals. In the early stages of the air traffic sequence, feature-based inbound management and navigation are used to expand the design horizons and make it possible to study traffic in both take-off and landing positions. The aircraft landing sequence is centralized by examining parameters such as reducing apron and parking costs, minimizing latency and early arrival times, and minimizing fuel consumption costs. Finally, this paper intends to obtain a new way to improve the quality of scheduling programs and reduce their execution time by using theoretically validated algorithms and adapting them to the challenges of scheduling. Experimental experiments were performed on the data set of Mashhad Shahid Hasheminejad International Airport and showed that the main scheduling objectives can be better achieved with this method.

References:

1. Bencheikh, G., Boukachour, J., & Alaoui, A. E. H. (2016). A memetic algorithm to solve the dynamic multiple runway aircraft landing problem. Journal of King Saud University-Computer and Information Sciences, 28(1), 98-109.


2. Bennell, J. A., Mesgarpour, M., & Potts, C. N. (2017). Dynamic scheduling of aircraft landings. European Journal of Operational Research, 258(1), 315-327.


3. Caprı̀, S., & Ignaccolo, M. (2004). Genetic algorithms for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model. Journal of Air Transport Management, 10(5), 345-351.


4. Deng, Q., Santos, B. F., & Curran, R. (2020). A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization. European Journal of Operational Research, 281(2), 256-273.


5. Deng, Q., Santos, B. F., & Curran, R. (2020). A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization. European Journal of Operational Research, 281(2), 256-273.


6. Ikli, S., Mancel, C., Mongeau, M., Olive, X., & Rachelson, E. (2020, June). Coupling Mathematical Optimization and Machine Learning for the Aircraft Landing Problem. In ICRAT 2020, 9th International Conference for Research in Air Transportation.


7. Li, L., Sun, L., Guo, J., Qi, J., Xu, B., & Li, S. (2017). Modified discrete grey wolf optimizer algorithm for multilevel image thresholding. Computational intelligence and neuroscience, 2017.


8. Liu, M., Liang, B., Zheng, F., Chu, C., & Chu, F. (2018, July). A two-stage stochastic programming approach for aircraft landing problem. In 2018 15th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-6). IEEE.


9. Mahmud, A. A., & Jebersen, W. (2017). Review on Dynamic Aircraft Scheduling. International Journal of Pure and Applied Mathematics, 117(21), 753-767.


10. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.


11. Mokhtarimousavi, S., Rahami, H., & Kaveh, A. (2015). Multi-objective mathematical modeling of aircraft landing problem on a runway in static mode, scheduling and sequence determination using NSGA-II. Iran University of Science & Technology, 5(1), 21-36.


12. Mokhtarimousavi, S., Rahami, H., Saffarzadeh, M., & Piri, S. (2014). Determination of the aircraft landing sequence by two meta-heuristic algorithms. International Journal of Transportation Engineering, 1(4), 271-284.


13. Mokhtarimousavi, S., Talebi, D., & Asgari, H. (2018). A non-dominated sorting genetic algorithm approach for optimization of multi-objective airport gate assignment problem. Transportation Research Record, 2672(23), 59-70.


14. Ng, K. K. H., Lee, C. K. M., Chan, F. T., & Qin, Y. (2017). Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach. Transportation Research Part E: Logistics and Transportation Review, 106, 115-136.


15. Prakash, R., Piplani, R., & Desai, J. (2018). An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput. Transportation Research Part C: Emerging Technologies, 95, 570-581.


16. Ren, Y., Lu, Z., & Liu, X. (2020). A branch-and-bound embedded genetic algorithm for resource-constrained project scheduling problem with resource transfer time of aircraft moving assembly line. Optimization Letters, 1-35.


17. Ruan, J. H., Wang, Z. X., Chan, F. T., Patnaik, S., & Tiwari, M. K. (2021). A reinforcement learning-based algorithm for the aircraft maintenance routing problem. Expert Systems with Applications, 169, 114399.


18. Salehipour, A., Modarres, M., & Naeni, L. M. (2013). An efficient hybrid meta-heuristic for aircraft landing problem. Computers & Operations Research, 40(1), 207-213.


19. Shahmoradi-Moghadam, H., Safaei, N., & Sadjadi, S. J. (2021). Robust Maintenance Scheduling of Aircraft Fleet: A Hybrid Simulation-Optimization Approach. IEEE Access, 9, 17854-17865.


20. Sylejmani, K., Bytyçi, E., & Dika, A. (2017). Solving aircraft sequencing problem by using genetic algorithms. Intelligent Decision Technologies, 11(4), 451-463.


21. Vadlamani, S., & Hosseini, S. (2014). A novel heuristic approach for solving aircraft landing problem with single runway. Journal of Air Transport Management, 40, 144-148.


22. Wei, M., Sun, B., Wu, W., & Jing, B. (2020). A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways. Mathematical Biosciences and Engineering, 17(5), 5545-5560.


23. Wei, M., Zhao, L., Ye, Z., & Jing, B. (2020). An integrated optimization mode for multi-type aircraft flight scheduling and routing problem [J]. Mathematical Biosciences and Engineering, 17(5), 4990-5004.


24. Xu, B. (2017). An efficient Ant Colony algorithm based on wake-vortex modeling method for aircraft scheduling problem. Journal of Computational and Applied Mathematics, 317, 157-170.


25. Yu, S. P., Cao, X. B., & Zhang, J. (2011). A real-time schedule method for Aircraft Landing Scheduling problem based on Cellular Automation. Applied Soft Computing, 11(4), 3485-3493.


26. ZHANG, J., ZHAO, P., YANG, C., & HU, R. (2020). A New Meta⁃ Heuristic Approach for Aircraft Landing Problem. Transactions of Nanjing University of Aeronautics and Astronautics. (37) 2, 197-208.


27. Zhang, J., Zhao, P., Zhang, Y., Dai, X., & Sui, D. (2020). Criteria selection and multi-objective optimization of aircraft landing problem. Journal of Air Transport Management, 82, 101734.


28. Zheng, S., Yang, Z., He, Z., Wang, N., Chu, C., & Yu, H. (2020). Hybrid simulated annealing and reduced variable neighbourhood search for an aircraft scheduling and parking problem. International Journal of Production Research, 58(9), 2626-2646.


29. Zulkifli, A., Aziz, N. A. A., Aziz, N. H. A., Ibrahim, Z., & Mokhtar, N. (2018). Review on computational techniques in solving aircraft landing problem. 128-131