Agent-based Modelling for Backup Aircraft Site Selection to Decrease Flights Delay Time: Case Study: Qeshm Airlines

Document Type : Original Article

Authors

1 Department of Surveying Engineering, Technical and Vocational University (TVU), Tehran, Iran.

2 Associate Professor of Geospatial Information System, Geodesy and Geomatics Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran.

10.48301/kssa.2022.334125.2047

Abstract

Flight delays are one of the most important and pervasive problems in the aviation industry. One way to reduce flight delays is to use backup planes. In previous research, less attention has been paid to the issue of backup aircraft and especially the location of their deployment in order to reduce air traffic delays. Therefore, in this research, an agent-based modelling and simulation is used to find the optimum deployment location of backup aircrafts. The main objectives of this study were to investigate the effect of locating backup aircraft as well as the effect of increasing their number in reducing the average delay time of air flights. In modeling, three scenarios were examined based on the number of backup aircraft. The first, second and third scenario consisted of one, two and three backup aircrafts, respectively. The results showed 26% and 29% increase in the number of backup aircraft from one to two aircrafts and from two to three aircrafts in reducing the average delay time. In addition, in each of the scenarios, the difference between the worst and best locations found for the deployment of backup aircrafts and its impact on latency was examined. This effect was 70%, 77% and 84% in the first, second and third scenarios, respectively. These huge differences showed the great impact of optimal location of the deployment centers in reducing the flight delays.

Keywords

Karafan Quarterly Scientific Journal
Volume 20, Issue 1 - Serial Number 61
Technical & Engineering
June 2023

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History

  • Receive Date: 12 April 2022
  • Revise Date: 09 June 2022
  • Accept Date: 21 June 2022

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