DocumentCode :
715798
Title :
Solving airport gate assignment problem using Genetic Algorithms approach
Author :
Ghazouani, Hammadi ; Hammami, Moez ; Korbaa, Ouajdi
Author_Institution :
MARS/RU, Univ. of Monastir, Monastir, Tunisia
fYear :
2015
fDate :
20-22 May 2015
Firstpage :
175
Lastpage :
180
Abstract :
Because of the rapid growth of air traffic, optimizing airport management is becoming necessary in order to improveairport´s capacity and better align its resources to the received traffic. In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers. The index of each gene represents the flight number and its value represents the gate to which the flight will be assigned. The method used to generate the initial population is based on three different heuristics and a random sorting of the gates. The selection method is the “In fitness proportionate selection” known as “roulette wheel selection”. In addition to one point and two point Crossover operators, we designed a Greedy procedure Crossover (GPX) operator. The experimentation is based on the use of fictive scenarios generated in accordance with the physical characteristics of the Tunis Carthage Airport and using different flight schedules. The comparison between deterministic approach, simple heuristics and the GA has shown the efficiency of the last approach in terms of solution´s quality when we aim at solving the problems of large size. In order to determine the best configuration of the GA, we compared the different crossover operators and we noticed that the use of GPX improves the speed of convergence of the algorithm towards better solutions.
Keywords :
air traffic; airports; convergence; genetic algorithms; GAP; GPX operator; Tunis Carthage Airport; airport gate assignment problem; airport management; convergence speed; flight schedule; genetic algorithm approach; greedy procedure crossover operator; Encoding; Logic gates; Mars; Genetic Algorithms; Greedy Partition Crossover (GPX); gate assignment; ground optimization; meta-heuristics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Logistics and Transport (ICALT), 2015 4th International Conference on
Conference_Location :
Valenciennes
Type :
conf
DOI :
10.1109/ICAdLT.2015.7136615
Filename :
7136615
Link To Document :
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