پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 628 |
| تعداد مقالات | 9,179 |
| تعداد مشاهده مقاله | 67,528,974 |
| تعداد دریافت فایل اصل مقاله | 8,063,787 |
A new improved gray wolf optimization algorithm to solve the aircraft landing problem at Mashhad Shahid Hasheminejad International Airport | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 39، دوره 13، شماره 2، مهر 2022، صفحه 435-445 اصل مقاله (433.78 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.23255.2510 | ||
| نویسندگان | ||
| Manijeh Teymori1؛ Houshang Taghizadeh* 1؛ Mortrza Azimi Honarmand1؛ Jafar Pourmahmoud2 | ||
| 1Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran. | ||
| 2Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran. | ||
| تاریخ دریافت: 06 اسفند 1399، تاریخ بازنگری: 30 فروردین 1400، تاریخ پذیرش: 07 اردیبهشت 1400 | ||
| چکیده | ||
| Air traffic management is a sensitive and stressful job with various daily problems and obstacles. The aircraft landing problem is one of the most important issues addressed currently in flight surveillance. This issue has several optimal local points. Gradient-based algorithms cannot produce an optimal solution in a reasonable time to solve this problem. Meta-heuristic algorithms are used to solve such problems. Since landing earlier or later than the scheduled time will lead to higher costs for each aircraft, this article aims at minimizing the time deviation from the originally scheduled landing time of each flight. The Gray Wolf Algorithm is a new meta-heuristic algorithm inspired by wolf behaviour. However, it has a problem with global and local searches. To solve this problem, the fitness of each wolf is assigned a weight and the new member is obtained using those weights. In addition, in order to increase the local and global search capability of the algorithm, if a condition with a probability of 0.3 is met, a random search is performed around the position of the best wolf. Otherwise, by setting a condition with a probability of 0.1, a global search is performed as the mutation operator around a selected wolf. This improves the algorithm's ability to search globally and locally. In order to evaluate this algorithm in solving the problem, its result is compared with the algorithms of particle swarm optimization, firefly and the common Gray Wolf. The results show a very high performance of this algorithm compared to other similar algorithms. | ||
| کلیدواژه‌ها | ||
| Aircraft Landing Problem (ALP)؛ Metaheuristic Algorithms؛ Improved Gray Wolf Algorithm | ||
| مراجع | ||
|
[1] G. Bencheikh, J. Boukachour and A.E.H. Alaoui, A memetic algorithm to solve the dynamic multiple runway aircraft landing problem, J. King Saud Uni.-Comput. Inf. Sci. 28 (2016), no. 1, 98–109. [2] J.A. Bennell, M. Mesgarpour and C.N. Potts, Dynamic scheduling of aircraft landings, European J. Oper. Res. 258 (2017), no. 1, 315–327. [3] S. Capr´i and M. Ignaccolo, Genetic algorithms for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model, J. Air Transport Manag. 10 (2004), no. 5, 345–351. [4] Q. Deng, B.F. Santos and R. Curran, A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization, Eur. J. Oper. Res. 281 (2020), no. 2, 256–273. [5] S. Ikli, C. Mancel, M. Mongeau, X. Olive and E. Rachelson, Coupling mathematical optimization and machine learning for the aircraft landing problem, ICRAT 2020, 9th International Conference for Research in Air Transportation, 2020. [6] L. Li, L. Sun, J. Guo, J. Qi, B. Xu and S. Li, Modified discrete grey wolf optimizer algorithm for multilevel image thresholding, Comput. Intell. Neurosci. 2017 (2017). [7] M. Liu, B. Liang, F. Zheng, C. Chu and F. Chu, A two-stage stochastic programming approach for aircraft landing problem, 15th Int. Conf. Serv. Syst. Serv. Manag. (ICSSSM), IEEE, 2018, pp. 1–6. [8] A.A. Mahmud and W. Jebersen, Review on dynamic aircraft scheduling, Int. J. Pure Appl. Math. 117 (2017), no. 21, 753–767. [9] S. Mirjalili, S.M. Mirjalili and A. Lewis, Grey wolf optimizer, Adv. Engin. Software 69 (2014), 46–61. [10] S. Mokhtarimousavi, H. Rahami and A. Kaveh, Multi-objective mathematical modeling of aircraft landing problem on a runway in static mode, scheduling and sequence determination using NSGA-II, Iran Univ. Sci. Technol. 5 (2015), no. 1, 21–36. [11] S. Mokhtarimousavi, H. Rahami, M. Saffarzadeh and S. Piri, Determination of the aircraft landing sequence by two meta-heuristic algorithms, Int. J. Transport. Eng. 1 (2014), no. 4, 271–284. [12] S. Mokhtarimousavi, D. Talebi and H. Asgari, A non-dominated sorting genetic algorithm approach for optimization of multi-objective airport gate assignment problem, Transport. Res. Record 2672 (2018), no. 23, 59–70. [13] K.K.H. Ng, C.K.M. Lee, F.T. Chan and Y. Qin, Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach, Transport. Res. Part E: Logist. Transport. Rev. 106 (2017), 115–136. [14] R. Prakash, R. Piplani and J. Desai, An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput, Transportation Research Part C: Emerging Technologies, 95 (2018), 570–581. [15] Y. Ren, Z. Lu and X. Liu, A branch-and-bound embedded genetic algorithm for resource-constrained project scheduling problem with resource transfer time of aircraft moving assembly line, Optim. Lett. 14 (2020), no. 8, 2161–2195. [16] J.H. Ruan, Z.X. Wang, F.T. Chan, S. Patnaik and M.K. Tiwari, A reinforcement learning-based algorithm for the aircraft maintenance routing problem, Expert Syst. Appl. 169 (2021), 114399. [17] A. Salehipour, M. Modarres and L.M. Naeni, An efficient hybrid meta-heuristic for aircraft landing problem, Comput. Oper. Res. 40 (2013), no. 1, 207–213. [18] H. Shahmoradi-Moghadam, N. Safaei and S.J. Sadjadi, Robust maintenance scheduling of aircraft fleet: a hybrid simulation-optimization approach, IEEE Access 9 (2021), 17854–17865. [19] K. Sylejmani, E. Byty¸ci and A. Dika, Solving aircraft sequencing problem by using genetic algorithms, Intell. Decision Technol. 11 (2017), no. 4, 451–463. [20] S. Vadlamani and S. Hosseini, A novel heuristic approach for solving aircraft landing problem with single runway, J. Air Transport Manag. 40 (2014), 144–148. [21] M. Wei, B. Sun, W. Wu and B. Jing, A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways, Math. Biosci. Eng. 17 (2020), no. 5, 5545–5560. [22] M. Wei, L. Zhao, Z. Ye and B. Jing, An integrated optimization mode for multi-type aircraft flight scheduling and routing problem [J], Math. Biosci. Eng. 17 (2020), no. 5, 4990–5004. [23] B. Xu, An efficient ant colony algorithm based on wake-vortex modeling method for aircraft scheduling problem, J. Comput. Appl. Math. 317 (2017), 157–170. [24] S.P. Yu, X.B. Cao and J. Zhang, A real-time schedule method for aircraft landing scheduling problem based on cellular automation, Appl. Soft Comput. 11 (2011), no. 4, 3485–3493. [25] J. Zhang, P. Zhao, C. Yang and R. Hu, A new meta-heuristic approach for aircraft landing problem [J], Trans. Nanjing Univ. Aeronautics and Astronautics 37 (2020), no. 2, 197–208. [26] J. Zhang, P. Zhao, Y. Zhang, X. Dai and D. Sui, Criteria selection and multi-objective optimization of aircraft landing problem, J. Air Transport Manag. 82 (2020), p. 101734. [27] S. Zheng, Z. Yang, Z. He, N. Wang, C. Chu and H. Yu, Hybrid simulated annealing and reduced variable neighbourhood search for an aircraft scheduling and parking problem, Int. J. Product. Res. 58 (2020), no. 9, 2626–2646. [28] A. Zulkifli, N.A.A. Aziz, N.H.A. Aziz, Z. Ibrahim and N. Mokhtar, Review on computational techniques in solving aircraft landing problem, Proc. Int. Conf. Artific. Life Robotics, Oita, Japan, 2018. | ||
|
آمار تعداد مشاهده مقاله: 44,052 تعداد دریافت فایل اصل مقاله: 466 |
||