IMPROVING THE GEVGELIJA AERODROME PROJECT METHODOLOGY THROUGH THE EYE OF LOGIC OPTIMIZATION

Abstract

The term equilibrium and the emergence of new technologies intended for aerodrome infrastructure development puts increasing pressure on the field of optimized traffic networks as crucial criteria for location selection. There is a constant need to improve optimization processes as one of the needed solutions for an equalized system. However, finding and implementing an optimization model is a potential requirement for traffic capacity for planned aerodromes where the intended capacity has the decision rule. In this paper, the authors show how the optimization model may be recast as a decision factor. We then take advantage of the recent advantage of recent advantages in Single European Sky ATM Research- SESAR reinforcement learning to build a project methodology that learns how the new aerodrome infrastructure can be born. Our design for the Gevgelija aerodrome has a number of desirable locations and it is decisional for one that generalizes many decision factors. Additionally, this methodology natively supports problems like this one, without the need to handle special cases. Finally, it is the same methodology that can be used to achieve different optimization objectives, e.g. aerodrome category and aerodrome development.