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International Journal of Transportation Engineering، جلد ۱، شماره ۴، صفحات ۲۷۱-۲۸۴
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عنوان انگلیسی Determination of the Aircraft Landing Sequence by Two Meta-Heuristic Algorithms
چکیده انگلیسی مقاله Due to an anticipated increase in air traffic during the next decade, air traffic control in busy airports is one of the main challenges confronting the controllers in the near future. Since the runway is often a bottleneck in an airport system, there is a great interest in optimizing the use of the runway. The most important factors in aircraft landing modeling are time and cost. For this reason, Aircraft Landing Scheduling Problem (ASLP) is a typical hard multi-constraint optimization problem and finding its efficient solution would be very difficult. So in real applications finding the best solution is not the most important issue and providing a feasible landing schedule in an acceptable time would be the preferred requirement. In this study a three objectives formulation of the problem proposed as a mathematical programming model on a runway in static mode. Problem is solved by multi-objective genetic algorithm (NSGA-II) and multi-objective Particle Swarm Optimization Algorithm (MOPSO). Considering a group of 20 aircrafts, this problem is solved and landing sequence determined and we are shown the obtained sequence does not follow First Come First Serve law for sequencing as well. Finally by comparing results, conclusion and suggestions are proposed.
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نویسندگان مقاله Seyedmirsajad Mokhtarimousavi |
MSc. Student of Transportation Planning, Science and Research Branch, Islamic Azad University, Tehran, Iran

Hossein Rahami |
Assistant Professor, School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Mahmoud Saffarzadeh |
Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Iran

Saeed Piri |
Ph.D. Student, Department of Industrial Engineering and Management, Oklahoma State University, Oklahoma, USA.

نشانی اینترنتی http://www.ijte.ir/article_6299_abbd8c58a8fb8066b96850be3334a28e.pdf
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