Flamingo Search Algorithm for aircraft landing scheduling

Authors

  • Zied O. Ahmed Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq. https://orcid.org/0000-0002-9141-7543
  • Noor T. Mahmood Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq.
  • Sura Mazin Ali Political Science College, Mustansiriyah University, Baghdad, Iraq.

DOI:

https://doi.org/10.21123/bsj.2024.8689

Keywords:

Aircraft Landing Scheduling, Flamingo Search Algorithm, Genetic Algorithm, metaheuristic, Optimization

Abstract

Aircraft landing scheduling (ALS) is the process of organizing the arrival and departure of aircraft at an airport. This process is managed by air traffic controllers who use various tools and techniques to ensure that aircraft land and take off safely and efficiently. The goal of aircraft landing scheduling is to minimize delays and maximize the number of aircraft that can be accommodated at the airport. This is done by carefully coordinating the arrival and departure times of aircraft and the number of aircraft that can be safely accommodated. Flamingo Search is an optimization algorithm stimulated by the flamingos’ behavior. A population-based metaheuristic algorithm uses a flock of flamingos to search for the optimal solution to a given problem. The algorithm works by having each flamingo in the flock search for a local optimum solution. The flamingos then communicate with each other and share their solutions. Experiments have demonstrated that our solution is significantly faster and more appropriate for real-time ALS problems compared to conventional optimization techniques, the results showed the superiority of the proposed algorithm over the rest of the algorithms by more than 90%. The suggested method can quickly identify appropriate solutions for all 8 data sets.

References

The international air transport association (IATA). [Online]

Julia AB, Mohammad M, Chris NP. Dynamic scheduling of aircraft landings. Eur J Oper Res. 2017; 258(1): 315-327. https://doi.org/10.1016/j.ejor.2016.08.015.

Meriem BM. A thorough review of aircraft landing operation from practical and theoretical standpoints at an airport which may include a single or multiple runways. Appl Soft Comput. 2021; 98(2): 106853. https://doi.org/10.1016/j.asoc.2020.106853.

Wang Z, Liu J.Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm.in IEEE Access. 2021; 9(2): 88564-88582. https://doi.org/10.1109/ACCESS.2021.3090512.

Vincent B, Jobish V, Xavier CR, Angélica S.The generalized flexible job shop scheduling problem, Comput Ind Eng. 2021; 160(3): 107542. https://doi.org/10.1016/j.cie.2021.107542.

Yibing L, Weixing H, Rui W, Kai G. An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem. Appl Soft Comput. 2020; 95(2): 106544. https://doi.org/10.1016/j.asoc.2020.106544.

Anas N, Adnene H, Monia R, Robert P. A Didactic Review On Genetic Algorithms For Industrial Planning And Scheduling Problems. IFAC-Papers On Line.2022; 55(10): 2593-2598. https://doi.org/10.1016/j.ifacol.2022.10.100.

Ahmed TS, Samer AH. Two Improved Cuckoo Search Algorithm to solve Flexible Job Shop Scheduling Problem. IJPCC. 2016; 2(2): 2456-2490. https://doi.org/10.31436/ijpcc.v2i2.34

Ahmed TS, Samer AH. An improved Artificial Fish Swarm Algorithm to solve flexible job shop.Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), Baghdad, Iraq. 2017; 9(3): 7-12. https://doi.org/10.1109/NTICT.2017.7976155.

Zied OA, Ahmed TS, Hasanen SA. Solving the Traveling Salesman's Problem Using Camels Herd Algorithm. 2nd Scientific Conference of Computer Sciences. SCCS, Baghdad, Iraq. 2019; 11(5): 1-5. https://doi.org/10.1109/SCCS.2019.8852596.

Phan HD, Ellis K, Barca JC, Jan CB, Alan D.A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms. Neural Comput Appl. 2020; 32(4): 567–588. https://doi.org/10.1007/s00521-019-04229-2

Zhao X, Wang G. Deep Q networks-based optimization of emergency resource scheduling for urban public health events. Neural Comput Appl. 2022; 35(1): 8823–8832. https://doi.org/10.1007/s00521-022-07696-2

Jaafaru M, Rohaida R, Nooraini Y. An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment. Baghdad Sci J. 2019; 16(2) :0515. https://doi.org/10.21123/bsj.2019.16.2(SI).0515

Iqbal Z, Ilyas R, Chan HY, Ahmed N. Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach. Baghdad Sci J. 2021; 18(4): 1465. https://doi.org/10.21123/bsj.2021.18.4(Suppl.).1465

Sana I, Catherine M, Marcel M, Xavier O, Emmanuel R. The aircraft runway scheduling problem: A survey. Comput Oper Res. 2021; 132(1): 105336. https://doi.org/10.1016/j.cor.2021.105336.

Shuangyuan S, Hegen X, Gongfa L. A no-tardiness job shop scheduling problem with overtime consideration and the solution approaches. Comput Ind Eng.2023; 178(1): 109115. https://doi.org/10.1016/j.cie.2023.109115.

Candice D, Houda T, Belgacem B, Bélahcène M. Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration.environmental consideration and resilience improvement, J Manuf Syst.2023; 67(1): 155-173. https://doi.org/10.1016/j.jmsy.2023.01.004.

Fatima G, Jean-Charles P.Résolution du problème d’ordonnancement de type Job-Shop généralisé par des heuristiques dynamiques. [Rapport de recherche] lip6. 1997.005, LIP6. 2020; 1(1): 02546218.

Béchet A, Rendón-Martos M, Rendón M, Amat J, Johnson A, Gauthier-Clerc M. Global economy interacts with climate change to jeopardize species conservation: The case of the greater flamingo in the Mediterranean and West Africa. Environ Conserv. 2012; 39(1): 1-3. https://doi.org/10.1017/S0376892911000488

Ayache F, Gammar AM, Chaouach M. Environmental dynamics and conservation of the flamingo in the vicinity of Greater Tunis, Tunisia: the case study of Sebkha Essijoumi. Earth Surf Process. Landforms. 2006; 31(1): 1674-1684. https://doi.org/10.1002/esp.1438

Beasley JE. OR-Library: Distributing Test Problems by Electronic Mail. J Oper Res Soc.1990; 41(11): 1069–1072. https://doi.org/10.2307/2582903

Downloads

Issue

Section

article

How to Cite

1.
Flamingo Search Algorithm for aircraft landing scheduling. Baghdad Sci.J [Internet]. [cited 2024 Jul. 3];22(1). Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8689