Three-Dimensional (3D) Flight Planning Optimization Using Genetic Algorithm Considering FCOM Data

Calvarico Bima Nugraha, Neno Ruseno

Abstract

Flight Planning is a document prepared by airline which consist of aircraft information, planned route, required fuel, carried load, weather forecast, etc. It needs to be submitted to ATC to get approval and then it will be used by pilot to guide the flight to reach the destination. Optimization in flight planning route is one of the essential factors in reducing fuel consumption to reduce cost and emission. The aim of this research is to optimize the flight planning route in Three-Dimensional approach using Genetic algorithm.

Genetic algorithms (GA) are widely used in optimizations that includes many parameters, thus it could be used in flight planning optimization. The concept of GA is a heuristic search approach that inspired by Darwin’s theory of natural evolution which mimics “Survival of the fittestâ€. The method starts with a hexagon size containing nodes of possible points connecting departure and arrival airports. In this research the nodes are extracted using Dijkstra algorithm from previous research. The algorithm used population size of 500 individuals that generated with 0.01 mutation rate, 100 generation cycle, and 20 elite size. The case study covers routes in area of Indonesia which are flights of Jakarta – Tanjung Pinang, Jakarta – Makassar, and Jakarta – Manado. The different aircraft gross weights are analyzed to study the effect of aircraft weight to the resulted flight route. The aircraft performance database from Flight Crew Operating Manual (FCOM) of A320 aircraft was used to calculate the fuel burn.

It is concluded that the algorithm able to find the optimal flight route as the best individual with range of cruise altitude from 35,000 to 39,000 ft. Results from Jakarta - Tanjung Pinang showed an average of fuel reduction of around 2.29% followed by Jakarta - Makassar with 13.28% and Jakarta - Manado with 15.68%. Although, the resulted altitude profile shows a fluctuation in the middle of route, in average it is a climb.

Keywords

3D Flight Planning, Trajectory Optimization, Genetic Algorithm, Flight Crew Operating Manual, Fuel Saving

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References

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DOI: http://dx.doi.org/10.30536/j.jtd.2021.v19.a3607

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