Work place: Department of Computer Science Faculty of Graduate Studies for statistical Researches, Cairo University, Egypt
E-mail: magedmamdouh@pg.cu.edu.eg
Website:
Research Interests: Artificial Intelligence, Computational Learning Theory
Biography
Maged Mamdouh, is a M.Sc. candidate at the Department of Computer science from the Faculty of Graduate Studies for Statistical Research (ISSR), Cairo University, Egypt. he was born in Egypt, Cairo in 1982. He received B.Sc. of business administration and information system from, Cairo University, Egypt, in 2004. In 2009, he had worked with Egypt Air Ground Services, in Cairo Airport, as a Business intelligence report designer and developer. His research interests are in artificial intelligent, resource allocation, machine learning.
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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