Hesham Hefny

Work place: Department of Computer Science Faculty of Graduate Studies for statistical Researches, Cairo University, Egypt.

E-mail: hehefny@ieee.org


Research Interests: Genetic algorithms, Data Mining, Neural Networks


Hesham A Hefny, received the B.Sc., M.Sc. and Ph.D. all in Electronics and Communication Engineering from Cairo University in 1987, 1991 and 1998 respectively. He is currently a professor of Computer Science at Faculty of Graduate Studies for Statistical Research (ISSR), Cairo University, Egypt. He is also the vice dean of graduate studies and researches of (ISSR). Prof. Hefny has authored more than 150 papers in international conferences, journals and book chapters. His major research interest includes: computational intelligence (neural networks – Fuzzy systems-genetic algorithms – swarm intelligence), data mining , uncertain decision making. He is a member in the following professional societies: IEEE Computer, IEEE Computational Intelligence, and IEEE System, Man and Cybernetics.

Author Articles
Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction

By Maged Mamdouh Mostafa Ezzat Hesham Hefny

DOI: https://doi.org/10.5815/ijisa.2021.01.01, Pub. Date: 8 Feb. 2021

Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.

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