A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information.

Mostafa Borhani; Kamal Akbari; Aliakbar Matkan; Mohammad Tanasan

doi:10.9781/ijimai.2019.11.001

Authors

Mostafa Borhani Shahid Beheshti University
Kamal Akbari Shahid Beheshti University
Aliakbar Matkan Shahid Beheshti University
Mohammad Tanasan Shahid Beheshti University

DOI:

https://doi.org/10.9781/ijimai.2019.11.001

Keywords:

Artificial Intelligence, Geographic information system, Multi-Objective Genetic Algorithm (MOGA), Airway Topology, Non-Dominated Sorting Genetic Algorithm II (NSGA-II)

Abstract

Air route network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the flight network in air transport is analyzed with a multi-objective genetic algorithm regarding Geographic Information System (GIS) which is used to optimize this Iran airlines topology to reduce the number of airways and the aggregation of passengers in aviation industries organization and also to reduce changes in airways and the travel time for travelers. The proposed model of this study is based on the combination of two topologies – point-to-point and Hub-and-spoke – with multiple goals for causing a decrease in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. The proposed Multi-objective Genetic Algorithm (MOGA) is tested and assessed in data of the Iran airlines industry in 2018, as an example to real-world applications, to design Iran airline topology. MOGA is proven to be effective in general to solve a network-wide flight trajectory planning. Using the combination of point-to-point and Hub-and-spoke topologies can improve the performance of the MOGA algorithm. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 kilometers) and up to 18%, respectively. The proposed algorithm also suggests that the current air routes of Iran can be decreased up to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 kilometers) and 5%, respectively. Two intermediate airports were supposed for these experiments. The computational results show the potential benefits of the proposed model and the advantage of the algorithm. The structure of the flight network in air transport can significantly reduce operational cost while ensuring the operation safety. According to the results, this intelligent multi-object optimization model would be able to be successfully used for a precise design and efficient optimization of existing and new airline topologies.

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