An Optimization Of Flight Scheduling Using A Deep Learning Approach Utilizing Root Mean Square Propagation In Adjusting Routes And Time For Operational Efficiency

Donna Nm Sirait, M. Amril, Ivana Wardani, Darmeli Nasution, Yosei Ht Simanjuntak, Fahri Septiadi


This research explores the application of a Deep Learning approach by utilizing the Root Mean Square Propagation (RMSprop) algorithm to increase efficiency in flight scheduling. The main focus of the research is on adjusting routes and times to achieve higher operational efficiency in the aviation industry. Aviation is an important aspect of transportation that requires accurate and efficient scheduling to achieve punctuality, operational efficiency and a better passenger experience. This research aims to improve flight scheduling by applying Deep Learning techniques, specifically using the RMSprop algorithm. The research method includes the use of historical flight data to train the model, adjusting routes and times based on the analysis carried out by the RMSprop algorithm. The research results are expected to provide new insights into the application of Deep Learning technology in the aviation industry, with the aim of improving punctuality, reducing flight delays, and increasing the overall efficiency of airline operations. Thus, this research is expected to provide a valuable contribution in the development of a scheduling system that is more adaptive and responsive to the dynamics that occur in flight operations.


aviation, optimization, algorithms, deep learning, RMSprop


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DOI: 10.33751/jhss.v8i2.9711


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