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

Abstract

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.

Cold Chain Logistics Demand Forecasting Based on Improved BP Neural Network Model

“…For example, in the industrial sector, Awad et al constructed artificial neural networks using different types of optimization algorithms, successfully predicting the water demand in the Jenin city of Palestine [1]. In the aviation field, Mamdouh et al utilized machine learning to build a model for predicting ground service resource demand by constructing future flight schedule resource demand curves, which has been proven to have good accuracy [2]. In the medical field, Howlader et al compared naive Bayes, decision trees, random forests, and logistic regression using data mining techniques and accurately predicted heart diseases [3].…”