Model Optimasi Novel Permasalahan Perencanaan Lalu Lintas Udara pada Masa Pandemi Berbasis Pendekatan Deep Learning

Abstract

This research is a continuation of research originating from several previous studies, and ATFM problems from a mathematical optimization perspective. The model built in this research is how to schedule flights with minimal costs, so that capacity limitations at airports and airspace are always normal even in a pandemic situation. The basic logic of this research is that traffic patterns in airspace are influenced by the spatial and temporal dependencies of the traffic situation. Spatial dependence comes from traffic flows in the surrounding area. Flights in adjacent areas can be located in the studied airspace or future adjacent areas according to the purpose of the flight. Traffic flow in the airspace under study is also influenced by past traffic situations, namely the temporal dependence of traffic flow. Flights can remain in the current airspace or fly to adjacent areas in the future. The results of this research are in the form of a model. The aim of this research is to create an optimization model or mathematical model to determine flight scheduling based on destination and number of passengers and apply this optimization model to flight routes during the pandemic. The result of this research is a flight scheduling optimization model for operational cost efficiency and airline revenue based on the number and destination of passengers during the pandemic.