Gate Allocation Based on a Combination of Gray Wolf Algorithm and Fuzzy Logic System

Document Type : Scientific - Research

Authors

1 Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

2 Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

3 Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

Abstract

Air travel delays are inevitable and play a very important role in the profits and losses of airlines. By predicting or estimating delays, the benefits of agencies and customer satisfaction can be increased to some extent. Flight delays change flight schedules as well as increase organization costs. In this regard, a lot of work has been done, including scheduling and reallocation of gates, most of which tried to allocate the gate without considering flight delays and only due to limitations and the use of available resources. Since the variables affecting the gate allocation are uncertain and there is no known algorithm to find the optimal solution for it in a limited time, this issue is considered NP-hard problem and is subject to different conditions and policies of airports and agencies. In this paper, in case of delay, a hybrid system based on the Gray Wolf algorithm and fuzzy allocation logic is designed, which up to date the gate allocation list according to the uncertainties and using the data related to the delay prediction. The proposed model was implemented and tested using Boston data in a MATLAB environment. The results of the model evaluation showed that the proposed model has improved the gate allocation list so that some gates remain empty, which means that the number of flights can be increased and the organization's profit can be increased without changing the physical infrastructure of the airport.

Keywords

References

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Quarterly Journal of Transportation Engineering
Volume 14, Issue 3 - Serial Number 56
March 2023
Pages 2695-2711

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History

  • Receive Date: 22 September 2021
  • Accept Date: 12 December 2021

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