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Abdallah A. Abouzeid
Al Azhar University

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Particle swarm optimization for airlines fleet assignment Abdallah A. Abouzeid; Mostafa Mohei Eldin; Mohammed Abdel Razek
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp427-434

Abstract

Airline fleet assignment is the process of assigning aircraft types to scheduled flight legs in order to minimize operating cost and achieve maximize revenue, while satisfying a set of constraints. This paper formulate the fleet assignment problem for airlines that optimization goal is to minimize the total assignment cost. Particle swarm optimization proposed to solve this model. The model successfully applied to Egyptair airline dataset using the particle swarm optimization and mixed integer programming. The proposed method compared with mixed integer programming and current Egyptair assignment methodology. The results showed that the particle swarm optimization is the best method for the Egyptair fleet assignment process. The solution quality is better than mixed integer programming and Egyptair assignment methodology where we saw a daily cost reduction with a percentage of 14.6% and 19.3% respectively.
Airlines fleet assignment prediction model for new flights using deep neural network Abdallah A. Abouzeid; Mostafa Mohei Eldin; Mohammed Abdel Razek
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp973-980

Abstract

Airline fleet assignment is the process of allocating different types of aircraft to different scheduled flight legs in order to reduce operating costs and increase revenue. In this research, flights data records from Egypt Air airlines was employed to build an intelligent fleet assignment model to predict the optimal fleet type for new flights. Deep neural network (DNN) and support vector machines (SVM) was used for model formulations. We evaluated the performance of models on a fleet type prediction. The research results showed that various accuracy levels of fleet type multiclass classifications were attained by the models. In terms of accuracy, the deep neural network performed better than support vector machines. Besides, airline companies can use our proposed model for fleet type prediction for new flight with desired parameter values 5, 20 and 250 for hidden layers, number of neuron and number of epochs respectively if they use the same structure for data attributes.
Co-Authors Mohammed Abdel Razek Mostafa Mohei Eldin